1+e ?βx ?1,the bipolar semi-linear activation function,where βis the steepness parameter (that
de?nes the slope of h (x ));
g (x )=x ,the standard linear activation function;
W (resp.?W ),the weight of connections associated
with positive (resp.negative)literals;
θl ,the threshold of hidden neuron N l associated with
clause C l ;
θA ,the threshold of output neuron A ,where A is the
head of clause C l ;
k l ,the number of literals in the body of clause C l ;
p l ,the number of positive literals in the body of
clause C l ;n l ,the number of negative literals in the body of clause C l ;μl ,the number of clauses in P with the same atom in the head for each clause C l ;max C l (k l ,μl ),the greater element among k l and μl for clause C l ,max P (k 1,...,k q ,μ1,...,μq ),the greatest element among all k ’s and μ’s of P .For instance,for the program P of Example 2,q =3,m =6,k 1=3,k 2=2,k 3=0,p 1=2,p 2=2,p 3=0,n 1=1,n 2=0,n 3=0,μ1=2,μ2=2,μ3=1,max C 1(k 1,μ1)=3,max C 2(k 2,μ2)=2,max C 3(k 3,μ3)=1,and max P (k 1,k 2,k 3,μ1,μ2,μ3)=3.In the translation algorithm below,we de?ne A min =ξ1(k ,μ),W =ξ2(h (x ),k ,μ,A min ),θl =ξ3(k ,A min ,W ),and θA =ξ4(μ,A min ,W )such that condi-tions (1)and (2)are satis?ed,as we will see later in the proof of Theorem 3.Given a general logic program P ,consider that the literals of P are numbered from 1to m such that the input and output layers of N are vectors of maximum length m ,where the i th neuron represents the i th lite-ral of P .Assume,for mathematical convenience and without loss of generality,that A max =?A min .1.Calculate max P (k 1,...,k q ,μ1,...,μq )of P ;2.Calculate A min >max P (k 1,...,k q ,μ1,...,μq )?1max P (k 1,...,k q ,μ1,...,μq )+1;3.Calculate W ≥2β·ln (1+A min )?ln (1?A min )max P (k 1,...,k q ,μ1,...,μq )(A min ?1)+A min +1;4.For each clause C l of P of the form A ←L 1,...,L k (k ≥0):(a)Add a neuron N l to the hidden layer of N ;(b)Connect each neuron L i (1≤i ≤k )in the input layer to the neuron N l in the hidden layer.If L i is a positive literal then set the connec-tion weight to W ;otherwise,set the connection weight to ?W ;(c)Connect the neuron N l in the hidden layer to the neuron A in the output layer and set the connection weight to W ;(d)De?ne the threshold (θl )of the neuron N l in the hidden layer as θl =(1+A min )(k l ?1)W (e)De?ne the threshold (θA )of the neuron A in the output layer as θA =(1+A min )(1?μl )W .5.Set g (x )as the activation function of the neurons in the input layer of N .In this way,the activation of the neurons in the input layer of N ,given by each input vector i ,will represent an interpretation for P .
Connectionist Inductive Learning and Logic63
6.Set h(x)as the activation function of the neurons in
the hidden and output layers of N.In this way,a
gradient descent learning algorithm,such as back-
propagation,can be applied on N ef?ciently.
7.If N ought to be fully-connected,set all other con-
nections to zero.
Since N contains a bipolar semi-linear(differen-
tiable)activation function h(x),instead of a binary
threshold non-linear activation function,the network’s
output neurons activations are real numbers in the range
[?1,1].Therefore,we say that an output within the
range[A min,1]represents the truth-value“true”,while an output within[?1,?A min]represents“false”.We
will see later in the proof of Theorem3that the above
de?ned weights and thresholds do not allow the net-
work to present activations in the range(?A min,A min). Note that the translation of facts of P into N,for instance B←in Example2,is done by simply tak-ing k=0in the above algorithm.Alternatively,each fact of the form A←may be converted to a rule of the form A←T that is inserted in N using k=1, where T denotes“true”,and is an extra neuron that is always active in the input layer of N,i.e.,T has input data?xed at“1”.From the point of view of the computation of P by N,there is absolutely no differ-ence between the above two ways of inserting facts of P into N.However,considering the subsequent pro-cess of inductive learning,regarding P as background knowledge,if A←is inserted in N then the set of examples to be learned afterwards can defeat that fact by changing weights and/or establishing new connec-tions in N.On the other hand,if A←T is inserted in N then A can not be defeated by the set of examples since the neuron T is clamped in N.Defeasible and nondefeasible knowledge can therefore be respectively inserted in the network by de?ning variable and?xed weights.
The above translation algorithm is based upon the
one presented in[11],where N is de?ned with bi-
nary threshold neurons.It is known that such networks
have limited ability to learn.Here,in order to perform
inductive learning ef?ciently,N is de?ned using the
activation function h(x).An immediate result is that N can also perform inductive learning from examples and background knowledge as in[16].Moreover,the restriction imposed over W in[11],where it is shown that N computes T P for W=1,is weakened here, since the weights must be able to change during train-ing.
Nevertheless,in[16],and more clearly in[21],the
background knowledge must have a“suf?ciently small”
number of rules as well as a“suf?ciently small”number
of antecedents in each rule3in order to be accurately
encoded in the neural network.Unfortunately,these
restrictions become quite strong or even unfeasible if,
for instance,A max=1as in([16],Section5:Empiri-cal Tests of KBANN).Consequently,an interpretation
that does not satisfy a clause can wrongly activate a
neuron in the output layer of N.This results from
the use of the standard(unipolar)semi-linear activa-
tion function,where each neuron’s activation is in the
range[0,1].Hence,in[16]both“false”and“true”are
represented by positive numbers in the ranges[0,A max]
and[A min,1]respectively.For example,if A min=0.7 and k=2,an interpretation that assigns“false”to positive literals in the input layer of N can generate a positive input potential greater than the hidden neu-ron’s threshold,wrongly activating the neuron in the output layer of N.
In order to solve this problem we use bipolar ac-
tivation functions,where each neuron’s activation is
in the range[?1,1].Now,an interpretation that does
not satisfy a clause contributes negatively to the hidden
neuron’s input potential,since“false”is represented by
a number in[?1,?A min],while an interpretation that
does satisfy a clause contributes positively to the in-
put potential,because“true”is in[A min,1].Theorem3 will show that the choice of a bipolar activation function is suf?cient to solve the above problem.Furthermore, the choice of?1instead of zero to represent“false”will lead to faster convergence in almost all cases.The rea-son is that the update of a weight connected to an input variable will be zero when the corresponding variable is zero in the training pattern[5,22].
Thus making use of bipolar semi-linear activation
function h(x),let us see how we have obtained the
values for the hidden and output neurons’thresholds θl andθA.To confer symmetric mathematical results, without loss of generality,assume that A max=?A min. From the input to the hidden layer of N(L1,...,L k?N l),if an interpretation satis?es L1,...,L k then the contribution of L1,...,L k to the input potential of N l is greater than I+=k A min W.If,conversely,an interpre-tation does not satisfy L1,...,L k then the contribution of L1,...,L k to the input potential of N l is smaller than I?=(p?1)W?A min W+nW.Therefore,we de?ne θl=I++I?
2
=(1+A min)(k?1)
2
W(translation algorithm,
step4d).From the hidden to the output layer of N
(N l?A),if an interpretation satis?es N l then the
64Garcez and Zaverucha
contribution of N
l to the input potential of A is greater
than I +=A min W ?(μ?1)W .If,conversely,an
interpretation does not satisfy N l then the contribu-
tion of N l to the input potential of A is smaller
than I ?=?μA min W .Similarly,we de?ne θA =I ++I ?
=
(1+A min )(1?μ)2W (translation algorithm,step 4e).Obvi-
ously,I +>I ?should be satis?ed in both cases above.
Therefore,A min >k l ?1l +1and A min >μ
l ?1
μl +1must i?ed and,more generally,the condition imposed over
A min in the translation algorithm (step 2).Finally,given
A min ,the value of W (translation algorithm (step 3))re-
sults from the proof of Theorem 3below.
In what follows,we show that the theorem presented
in [11],where N with binary threshold neurons com-
putes the ?xed point operator T P of the program P ,
still holds for N with semi-linear neurons.The fol-
lowing theorem ensures that our translation algorithm
is sound.The function T
P mapping interpretations to
interpretations is de?ned as follows.Let i be an inter-
pretation and A an atom.T
P (i )(
A )=“true ”iff there
exists A ←L 1,...,L k in P s .t . k i =1i (L i )=“true ”.
Theorem 3.For each propositional general logic
program P ,there exists a feedforward arti?cial neural
network N with exactly one hidden layer and semi-
linear neurons ,obtained by the above “Translation
Algorithm ”,such that N computes T P .
Proof:We have to show that there exists W >0such
that N computes T
P .In order to do so we need to prove
that given an input vector i ,each neuron A in the output
layer of N is “active”if and only if there exists a clause
of P of the form A ←L 1,...,L k s.t.L 1,...,L k are
satis?ed by interpretation i .The proof takes advantage
of the monotonically non-decreasing property of the
bipolar semi-linear activation function h (x ),which al-
lows the analysis to focus on the boundary cases.As
before,we assume that A max =?A min without loss of
generality.
(←)A ≥A min if L 1,...,L k is satis?ed by i .As-
sume that the p positive literals in L 1,...,L k are
“true”,while the n negative literals in L 1,...,L k are
“false”.Consider the mapping from the input layer
to the hidden layer of N .The input potential (I l )of
N l is minimum when all the neurons associated with a
positive literal in L 1,...,L k are at A min ,while all the neurons associated with a negative literal in L 1,...,L k
are at ?A min .Thus,I l ≥p A min W +n A min W ?θl
and assuming θl =(1+A min )(k ?1)2W ,I l ≥p A min W
+
n A min W ?(1+A min )(k ?1)2W .If h (I l )≥A min ,i.e.,I l ≥?1βln (1?A min min ),then N l is active.Therefore,Eq.(1)must be satis?ed.p A min W +n A min W ?(1+A min )(k ?1)2W ≥?1βln 1?A min 1+A min (1)Solving Eq.(1)for the connection weight (W )yields Eqs.(2)and (3),given that W >0.W ≥?2β·ln (1?A min )?ln (1+A min )k (A min ?1)+A min +1(2)A min >k ?1k +1(3)Consider now the mapping from the hidden layer to the output layer of N .By Eqs.(2)and (3)at least one neuron N l that is connected to A is “active”.The input potential (I l )of A is minimum when N l is at A min ,while the other μ?1neurons connected to A are at ?1.Thus,I l ≥A min W ?(μ?1)W ?θl and assuming θl =(1+A min )(1?μ)2W ,I l ≥A min W ?(μ?1)W ?(1+A min )(1?μ)W .If h (I l )≥A min ,i.e.,I l ≥?1βln (1?A min 1+A min ),then A is active.Therefore,Eq.(4)must be satis?ed.A min W ?(μ?1)W ?(1+A min )(1?μ)2W ≥?1βln 1?A min 1+A min (4)Solving Eq.(4)for the connection weight W yields Eqs.(5)and (6),given that W >0.W ≥?2β·ln (1?A min )?ln (1+A min )μ(A min ?1)+A min +1(5)A min >μ?1μ+1(6)(→)A ≤?A min if L 1,...,L k is not satis?ed by i .Assume that at least one of the p positive literals in L 1,...,L k is “false”or one of the n negative literals in L 1,...,L k is “true”.Consider the mapping from the input layer to the hidden layer of N .The input potential (I l )of N l is maximum when only one neuron associ-ated to a positive literal in L 1,...,L k is at ?A min or when only one neuron associated to a negative literal in L 1,...,L k is at A min .Thus,I l ≤(p ?1)W ?A min W +nW ?θl or I l ≤(n ?1)W ?A min W +pW
阈值只是区分了两种情况的界限丆至于输出的是多大电势还取决于输出函数
Connectionist Inductive Learning and Logic 65?θl ,respectively,and assuming
θl =(1+A min )(k ?1)
2W ,
I l ≤(k ?1?A min )W ?(1+A min )(k ?1)
2W .
If ?A min ≥h (I l ),i.e.,?A min ≥2
?β(I l )?1,then
I l ≤?1
βln (1+A min 1?A min ),and so N l is not active.Therefore,
Eq.(7)must be satis?ed.
(k ?1?A min )W ?(1+A min )(k ?1)2
W ≤
?1ln 1?A min 1+A min
(7)
Solving Eq.(7)for the connection weight W yields
Eqs.(8)and (9),given that W >0.
W ≥2·ln (1+A min )?ln (1?A min )
k (A min min (8)
A min >k ?1
k +1(9)
Consider now the mapping from the hidden layer to
the output layer of N .By Eqs.(8)and (9),all neu-
rons N l that are connected to A are “not active”.The
input potential (I l )of A is maximum when all the
neurons connected to A are at ?A
min .Thus,I l ≤
?μA min W ?θl and
assuming θl =(1+A min
)(1?μ)2W ,
I l ≤?μA min W ?(1+A min )(1?μ)2
W .If ?A min ≥h (I l ),i.e.,?A min ≥2
?β(I
l )?1,then
I l ≤?1
βln (1+A min
min ),and so A is not active.Therefore,Eq.(10)must be satis?ed.
?μA min W ?(1+A min )(1?μ)2
W ≤
?1ln 1+A min
1?A min
(10)
Solving Eq.(10)for the connection weight W yields
Eqs.(11)and (12),given that W >0.
W ≥2β·ln (1+A min )?ln (1?A min )
μ(A min ?1)+A min +1
(11)
A min >μ?1
(12)
Notice that Eqs.(2)and (5)are equivalent to Eqs.(8)
and (11),respectively.Hence,the above theorem holds
if for each clause C l in P Eqs.(2)and (3)are satis?ed
by W and A min from the input to the hidden layer of
N ,while Eqs.(5)and (6)are satis?ed by W and A min
from the hidden to the output layer of N .In order to unify the weights in N for each clause C l of P given the de?nition of max C l (k l ,μl ),it is suf?cient that Eqs.(13)and (14)below are satis?ed by W and A min ,respectively.W ≥2β·ln (1+A min )?ln (1?A min )max C l (k l ,μl )(A min ?1)+A min +1(13)A min >max C l (k l ,μl )?1max C l (k l ,μl )+1(14)Finally,in order to unify all the weights in N for a program P given the de?nition of max P (k 1,...,k q ,μ1,...,μq ),it is suf?cient that Eqs.(15)and (16)are satis?ed by W and A min ,res-pectively.W ≥2β·ln (1+A min )?ln (1?A min )max P (k 1,...,k q ,μ1,...,μq )(A min ?1)+A min +1(15)A min >max P (k 1,...,k q ,μ1,...,μq )?1max P (k 1,...,k q ,μ1,...,μq )+1(16)As a result,if Eqs.(15)and (16)are satis?ed by W and A min ,respectively,then N computes T P .2Example 4.Consider the program P ={A ←B ,C ,not D ;A ←E ,F ;B ←}.Converting fact B ←to rule B ←T and applying the Translation Algorithm ,we obtain the neural network N of Fig.3.Firstly,
we Figure 3.The neural network N obtained by the translation over
P .Connections with weight zero are not shown.
66Garcez and Zaverucha
calculate max P(k1,...,k n,μ1,...,μn)=3(step1), and A min>0.5(step2).Then,suppose A min=0.6, we obtain W≥6.931/β(step3).Alternatively,sup-pose A min=0.7,then W≥4.336/β.Let us take A min=0.7and h(x)as the standard bipolar semi-linear activation function(β=1),then if W=4.5,N computes the operator T P of P.4
In the above example,the neuron B appears at both the input and the output layers of N.This indicates that there are at least two clauses of P that are link-ed through B(in the example:A←B,C,not D and B←),de?ning a dependency chain[23].We represent that chain in the network using the recurrent connec-tion W r=1to denote that the output of B must feed the input of B in the next learning or recall step.In this way,regardless of the length of the dependency chains in P,N always contains a single hidden lay-er,thus obtaining a better learning performance.5We will explain in detail the use of recurrent connections in Section3.In Section4we will compare the learning results of C-IL2P with KBANN’s,where the number of hidden layers is equal to the length of the greatest dependency chain in the background knowledge.
Remark1.Analogously to[11],for any logic pro-gram P,the time needed to compute T P(i)in the network is constant;equal to two time steps(one to compute the activations from the input to the hidden neurons and another from the hidden to the output neu-rons).A parallel computational model requiring p(n) processors and t(n)time to solve a problem of size n is optimal if p(n)×t(n)=O(T(n)),where T(n) is the best sequential time to solve the problem[24]. The number of neurons and connections in the network that corresponds to a program P is given respectively by O(q+r)and O(q·r),where q is the number of clauses and r is the number of propositional vari-ables(atoms)occurring in P.The sequential time to compute T P(i)is bound to O(q·r),and so the above parallel computational model is optimal.
3.Massively Parallel Deduction
and Inductive Learning
The neural network N can perform deduction and in-duction.In order to perform deduction,N is trans-formed into a partially recurrent network N r by connecting each neuron in the output layer to its corres-pondent neuron in the input layer with weight W r=
1,Figure4.The recurrent neural network N r.
as shown in Fig.4.In this way,N r is used to iterate T P
in parallel,because its output vector becomes its input
vector in the next computation of T P.
Let us now show that as in[11],if P is an accept-
able program then N r always settles down in a stable
state that yields the unique?xed point of T P,since N r
computes the upward powers(T m P(i))of T P.A simi-lar result could also be easily proved for the class of
locally strati?ed programs(see[12]).
De?nition5[13,23].Let B P denote the Herbrand
base of P,i.e.,the set of propositional variables(atoms)
occurring in P.A level mapping for a program P is a
function||:B P→?of ground atoms to natural num-bers.For A∈B P,|A|is called the level of A and |not A|=|A|.
De?nition6[13,23].Let P be a program,||a level mapping for P,and i a model of P.P is called accept-able w.r.t||and i if for every clause A←L1,...,L k in P the following implication holds for1≤i≤k:if i|=
i?1
j=1
L j then|A|>|L j|.
Theorem7[14].For each acceptable general pro-gram P,the T P has a unique?xed-point.The sequence of all T m P(i),m∈?,converges to this?xed-point T P(i)(which is identical to the stable model of P[15]),for each i?B P.
Recall that,since N r has semi-linear neurons,for each real value o i in the output vector(o)of N r,if o i≥A min then the corresponding i th atom in P is
Connectionist Inductive Learning and Logic67
assigned“true”,while o i≤A max means that it is as-signed“false”.
Corollary8.Let P be an acceptable general pro-gram.There exists a recurrent neural network N r with semi-linear neurons such that,starting from an arbi-trary initial input,N r converges to a stable state and yields the unique?xed-point(T P(i))of T P,which is identical to the stable model of P.
Proof:Assume that P is an acceptable program.By Theorem3,N r computes T P.Recurrently connected, N r computes the upwards powers(T m P(i))of T P.By Theorem7,N r computes the unique stable model of P (T P(i)).2
Hence,in order to use N as a massively parallel model for Logic Programming,we simply have to fol-low two steps:(i)add neurons to the input and output layers of N,allowing it to be partially recurrently con-nected;(ii)add the correspondent recurrent links with ?xed weight W r=1.
Example9(Example4continued).Given any ini-tial activation in the input layer of N r(Fig.4),it al-ways converges to the following stable state:A=“false”,B=“true”,C=“false”,D=“false”,E=“false”,and F=“false”,that represents the unique stable model of P:M(P)={B}.
One of the main features of arti?cial neural networks is their learning capability.The program P,viewed as background knowledge,may now be re?ned with examples in a neural training process on N r.Hornik et al.[25]have proved that standard feedforward neural networks with as few as a single hidden layer are capa-ble of approximating any(Borel measurable)function from one?nite dimensional space to another,to any desired degree of accuracy,provided suf?ciently many hidden units are available.Hence,we can train single hidden layer neural networks to approximate the oper-ator T P associated with a logic program P.Powerful neural learning algorithms have been established the-oretically and applied extensively in practice.These algorithms may be used to learn the operator T P of a previously unknown program P ,and therefore to learn the program P itself.Moreover,DasGupta and Schinitger[26]have proved that neural networks with continuously differentiable activation functions are ca-pable of computing a certain family of boolean func-tions with constant size(n),while networks composed of binary threshold functions require at least O(log(n)) size.Hence,analog neural networks have more com-putational power than discrete neural networks,even when computing boolean functions.
The network’s recurrent connections contain?xed weights W r=1,with the sole purpose of ensuring that the output feed the input in the next learning or recall process.As N r does not learn in its recurrent connections,6the standard backpropagation learning algorithm can be applied directly[22](see also[27]). Hence,in order to perform inductive learning with ex-amples on N r,four simple steps should be followed: (i)add neurons to the input and output layers of N r,ac-cording to the training set(the training set may contain concepts not represented in the background knowledge and vice-versa);(ii)add neurons to the hidden layer of N r,if it is so required for the learning algorithm conver-gence;(iii)add connections with weight zero,in which N r will learn new concepts;(iv)perturb the connections by adding small random numbers to its weights in order to avoid learning problems caused by symmetry.7The implementation of steps(i)–(iv)will become clearer in Section4,where we describe some applications of the C-IL2P system using backpropagation.
Remark2.The?nal stage of the C-IL2P system is the symbolic knowledge extraction from the trained network.It is generally accepted that“rules’extrac-tion”algorithms can provide the so called explanation capability for trained neural networks.The lack of explanation for their reasoning mechanisms is one of neural networks’main drawbacks.Similarly,the lack of clarity of trained networks has been a main reason for serious criticisms.The extraction of symbolic knowl-edge from trained networks can considerably amelio-rate these problems.It makes the knowledge learned accessible for an expert’s analysis and allows for justi?-cation of the decision making process.The knowledge extracted can be directly added to the knowledge base or used in the solution of analogous domains problems. Symbolic knowledge extraction from trained net-works is an extensive topic on its own.Some of the main extraction proposals include[11,20,28,29,30, 31,32,33](see[34]for a comprehensive survey).The main problem of the extraction task can be summa-rized as the quality×complexity trade-off,where the higher the quality of the extracted rule set,the higher the complexity of the extraction algorithm.In the con-text of the C-IL2P system,the extraction task is de?ned as follows.Assume that after learning,N encodes a
68Garcez and Zaverucha
knowledge P that contains the background knowledge
P expanded or revised by the knowledge learned with training examples.An accurate extraction procedure
derives P from N iff N computes T P .The extraction step of C-IL 2P is beyond the scope
of this paper,and the interested reader is referred to
[19].However,we would like to point out that there
is a major conceptual difference between our approach
and other extraction methods.We are convinced that an
extraction method must consider default negation in the
?nal rule set,and not only “if then else ”rules.Neural
networks’behavior is commonly nonmonotonic [35],
and therefore we can not expect to map it properly into
a set of rules composed of Horn clauses only.
4.Experimental Results
We have applied the C-IL 2P system in two real-world
problems in the domain of Molecular Biology;in par-
ticular the “promoter recognition ”and “splice-junction
determination ”problems of DNA sequence analysis.8
Molecular Biology is an area of increasing interest for
computational learning systems analysis and applica-
tion.Speci?cally,DNA sequence analysis problems
have recently become a benchmark for learning sys-
tems’performance comparison.In this section we
compare the experimental results obtained by C-IL 2P
with a variety of learning systems.
In what follows we brie?y introduce the problems
in question from a computational application perspec-
tive (see [36]for a proper treatment on the subject).
A DNA molecule contains two strands that are linear
sequences of nucleotides.The DNA is composed from
four different nucleotides—adenine ,guanine ,thymine ,
and cytosine —which are abbreviated by a ,g ,t ,c ,re-
spectively.Some sequences of the DNA strand,
called Figure 5.Inserting rule Minus 5←@?1‘gc ’,@5‘t ’into the neural network.
genes,serve as blueprint for the synthesis of proteins.Interspersed among the genes are segments,called non-coding regions,that do not encode proteins.Following [16]we use a special notation to iden-tify the location of nucleotides in a DNA sequence.Each nucleotide is numbered with respect to a ?xed,biologically meaningful,reference point.Rules’an-tecedents of the form “@3atcg ”state the location rel-ative to the reference point in the DNA,followed by the sequence of symbols that must occur.For exam-ple,“@3atcg ”means that an a must appear three nu-cleotides to the right of the reference point,followed by a t four nucleotides to the right of the reference point and so on.By convention,location zero is not used,while ‘?’means that any nucleotide will suf-?ce in a particular location.In this way,a rule of the form Minus 35←@?36‘ttg ?ca ’is a short representa-tion for Minus 35←@?36‘t ’,@?35‘t ’,@?34‘g ’,@?32‘c ’,@?31‘a ’.Each location is encoded in the network by four input neurons,representing nu-cleotides a ,g ,t and c ,in this order.The rules are therefore inserted in the network as depicted in Fig.5for the hypothetical rule Minus 5←@?1‘gc ’,@5‘t ’.In addition to the reference point notation,Table 1speci?es a standard notation for referring to all possible combinations of nucleotides using a single letter.This notation is compatible with the EMBL,GenBank,and Table 1.Single-letter codes for expressing uncertain DNA sequence.Code Meaning Code Meaning Code Meaning m a or c r a or g W a or t s c or g y c or t K g or t v a or c or g h a or c or t D a or g or t
b c or g or t x a or g or c or t
Connectionist Inductive Learning and Logic 69
Table 2.Background knowledge for promoter recognition.
Promoter ←contact ,conformation
Contact ←Minus 10,Minus 35
Minus 10←@?14‘tataat ’
Minus 35←@?37‘cttgac ’Minus 10←@?13‘tataat ’
Minus 35←@?36‘ttgaca ’Minus 10←@?13‘ta ?a ?t ’
Minus 35←@?36‘ttgac ’Minus 10←@?12‘ta ???t ’Minus 35←@?36‘ttg ?ca ’
Conformation ←@?45‘aa ??a ’
Conformation ←@?45‘a ???a ’,@?28‘t ???t ?aa ??t ’,@?4‘t ’
Conformation ←@?49‘a ????t ’,@?27‘t ????a ??t ?tg ’,@?1‘a ’
Conformation ←@?47‘caa ?tt ?ac ’,@?22‘g ???t ?c ’,@?8‘gcgcc ?cc ’
PIR data libraries—three major collections of data for
molecular biology.
The ?rst application in which we test C-IL 2P is
the prokaryotic 9promoter recognition.Promoters are
short DNA sequences that precede the beginning of
genes.The aim of “promoter recognition ”is to iden-
tify the starting location of genes in long sequences of
DNA.Table 2contains the background knowledge for
promoter recognition.10
The background knowledge of Table 2is translated
by C-IL 2P’s translation algorithm to the neural network
of Fig.6.In addition,two hidden neurons are added
in order to facilitate the learning of new concepts from
examples.Note that the network is fully-connected,
but low-weighted links are not shown in the ?gure.The
network’s input vector for this task contains 57consec-
utive DNA nucleotides.The training examples consist
of 53promoter and 53nonpromoter DNA sequences.
The second application that we use to test C-IL 2P
is eukaryotic 11splice-junction determination.Splice-
junctions are points on a DNA sequence at which the
non-coding regions are removed during the process
of Figure 6.Initial neural network for promoter recognition.Each box at the input layer represents one sequence location that is encoded by four input neurons {a ,g ,t ,c }.
protein synthesis.The aim of “splice-junction determi-nation ”is to recognize the boundaries between the part of the DNA retained after splice—called exons—and the part that is spliced out—the introns.The task con-sists therefore of recognizing exon/intron (E/I)bound-aries and intron/exon (I/E)boundaries.Table 3con-tains the background knowledge for splice junction determination.12The background knowledge of Table 3is trans-lated by C-IL 2P to the neural network of Fig.7.In Table 3,“?”indicates nondefeasible rules,which can not be altered during training.Therefore,the weights set in the network by these rules are ?xed.Rules of the form “m of (...)”are satis?ed if at least m of the parenthesized concepts are true.Note that the translation of these rules to the network is done by simply de?ning k l =m in C-IL 2P’s translation algo-rithm.Rules containing symbols other than the origi-nal (a ,g ,t ,c )are split into a number of equivalent rules containing only the original symbols,according to Table 1.For example,since y ≡c ∨t ,the rule IE ←pyramidine ?rich ,@?3‘yagg ’,not IE ?Stop is
70Garcez and Zaverucha
Table 3.Background knowledge for splice-junction.
EI ←@?3‘maggtragt ’,not EI ?Stop
EI ?Stop ?@?3‘taa ’EI ?Stop ?@?4‘taa ’
EI ?Stop ?@?5‘taa ’EI ?Stop ?@?3‘tag ’EI ?Stop ?@?4‘tag ’
EI ?Stop ?@?5‘tag ’EI ?Stop ?@?3‘tga ’
EI ?Stop ?@?4‘tga ’EI ?Stop ?@?5‘tga ’I E ←pyramidine ?rich ,@?3‘yagg ’,not I E ?Stop
pyramidine ?rich ←6of (@?15‘yyyyyyyyyy ’)
IE ?Stop ?@1‘taa ’
IE ?Stop ?@2‘taa ’IE ?Stop ?@3‘taa ’IE ?Stop ?@1‘tag ’
IE ?Stop ?@2‘tag ’IE ?Stop ?@3‘tag ’IE ?Stop ?@1‘tga ’IE ?Stop ?@2‘tga ’IE ?Stop ?@3‘tga
’
Figure 7.Initial neural network for splice-junction determination.Each box at the input layer of the network represents one sequence location which is encoded by four input neurons {a ,g ,t ,c }.
encoded in the network as IE ←pyramidine ?rich ,
@?3‘cagg ’,not IE ?Stop and IE ←pyramidine ?rich ,
@?3‘tagg ’,not IE ?Stop .The training set for this task contains 3190exam-
ples,in which approximately 25%are of I/E bound-
aries,25%are of E/I boundaries and the remaining
50%are neither.The third category (neither E/I nor
I/E)is considered true when neither I/E nor E/I output
neurons are active.Each example is a DNA sequence
with 60nucleotides,where the center is the reference
point.Remember that the network of Fig.7is fully-
connected,but that low-weighted links are not shown.
Dotted lines indicate links with negative weights.
In both applications,unless stated otherwise,the
background knowledge is assumed defeasible,i.e.,the
weights are allowed to change during the learning pro-
cess.Hence,some of the background knowledge may
be revised by the training examples.Note however
that the networks’recurrent connections are respon-
sible for reinforcing the background knowledge during
training.For instance,in the network of Fig.7the con-
cepts Pyramidine ,EI-St.and IE-St.,called intermedi-
ate concepts,have their input values calculated by the network in action,according to the background know-ledge and to the DNA sequence input vector.Let us now describe the experimental results ob-tained by C-IL 2P in the applications above.We com-pare it with other symbolic,neural and hybrid learn-ing systems.Brie?y,our tests show that C-IL 2P is a very effective system.Its test set performance is at least as good as KBANN’s,and therefore better than any method analyzed in [16].Moreover,C-IL 2P’s training set performance is considerably superior to KBANN’s,mainly because it always encodes the back-ground knowledge in a single hidden layer network.Firstly,let us consider C-IL 2P’s test-set perfor-mance,i.e.,its ability to generalize over examples not seen during training.We compare the results obtained by C-IL 2P in both applications with some of the main inductive learning systems from examples:Backpropa-gation [17],Perceptron [37](neural systems),ID3[38],and Cobweb [39](symbolic systems).We also com-pare the results in the promoter recognition problem with a method suggested by biologists [40].In addi-tion,we compare C-IL 2P with systems that learn from both examples and background knowledge:Either [41],
Connectionist Inductive Learning and Logic 71
Labyrinth-K [42],FOCL [43](symbolic systems),and
KBANN [16](hybrid system).13
As in [16],we evaluate the systems using cross-
validation ,a testing methodology in which the set of
examples is permuted and divided into n sets.One
division is used for testing and the remaining n ?1
divisions are used for training.The testing division is
never seen by the learning algorithm during the train-
ing process.The procedure is repeated n times so that
every partition is used once for testing.For the 106-
examples promoter data set,we use leaving-one-out
cross-validation,in which each example is successively
left out of the training set.Hence,it requires 106train-
ing phases,in which the training set has 105examples
and the testing set has 1example.Leaving-one-out
becomes expensive as the number of available exam-
ples grows.Therefore,following [16],we use 10-fold
cross-validation for the 1000-examples splice-junction
determination data set.14
The learning systems that are based on neural net-
works have been trained until one of the following
three Figure 8.Test-set performance in the promoter problem (comparison with systems that learn strictly from
examples).Figure 9.Test-set performance in the splice junction problem (comparison with systems that learn strictly from examples).stopping criteria was satis?ed:(i)on 99%of the training examples,the activation of every output unit is within 0.25of correctness;(ii)every training example is pre-sented to the network 100times,i.e.,the network has been trained for 100epochs;(iii)the network classi?es at least 90%of the training examples correctly but has not improved its classi?cation ability for 5epochs.We have de?ned an epoch as one training pass through the whole training set.We used the standard backpropa-gation learning algorithm to train C-IL 2P networks.C-IL 2P generalizes better than any empirical lear-ning system (see Figs.8and 9)and better than any system that learns from examples and background knowledge (see Figs.10and 11)tested on both ap-plications.In most cases differences are statistically signi?cant.However,C-IL 2P is only marginally better than KBANN.This is because both systems are hybrid neural systems that perform inductive learning from examples and background https://www.wendangku.net/doc/0516273789.html,ually,theory and data learning systems require fewer training examples than systems that learn only
72Garcez and
Zaverucha
Figure 10.Test-set performance in the promoter problem (comparison with systems that learn both from examples and
theory).Figure 11.Test-set performance in the splice junction problem (comparison with systems that learn both from examples and theory).from data.The background knowledge helps a learning
system to extract useful generalizations from small sets
of examples.This is quite important since,in general,
it is not easy to obtain large and accurate training sets.
Thus,let us now analyze C-IL 2P’s test-set perfor-
mance given smaller sets of examples.The following
tests will compare the performance of C-IL 2P with
KBANN and Backpropagation only,because these sys-
tems have shown to be the most effective ones in
the previous tests (Figs.8–11).Following [16],
the Figure 12.Test-set error rate in the promoter problem (26examples reserved for testing).
generalization ability over small sets of examples is analyzed by splitting the examples into two subsets:the testing set containing approximately 25%of the examples,and the training set containing the remain-ing examples.The training set is partitioned into sets of increasing sizes and the networks are trained using each partition at a time.Figures 12and 13show that in both applications C-IL 2P generalizes over small sets of examples bet-ter than backpropagation .The results empirically show
Connectionist Inductive Learning and Logic
73
Figure 13.Test-set error rate in the splice junction problem (798examples reserved for testing).
that the initial topology of the network,set by the back-
ground knowledge,gives it a better generalization ca-
pability.Note that the results obtained by C-IL 2P and
KBANN are very similar,since both systems are based
on the backpropagation learning algorithm and learn
from examples and background knowledge.
Concluding the tests,we check the training-set per-
formance of C-IL 2P in comparison again with
KBANN Figure 14.Training-set rms error decay during learning the promoter
problem.
Figure 15.Training-set rms error decay during learning the splice junction problem.
and backpropagation .Figures 14and 15describe the training-set rms error rate decay obtained by each sys-tem during learning respectively in each application.The rms parameter indicates how fast a neural network learns a set of examples w.r.t training epochs.Neu-ral networks’learning performance is a major concern,since it can become prohibitive in certain applications,usually as a result of the local minima problem.15
74Garcez and Zaverucha
Figures14and15show that C-IL2P’s learning per-formance is considerably better than KBANN’s.The results suggest that our translation algorithm from sym-bolic knowledge to neural networks has advantages over the algorithm presented in[16].The Translation Algorithm presented here always encodes the back-ground knowledge into a single hidden-layer neural network.However,KBANN’s translation algorithm generates a network with as many layers as there are dependency chains in the background knowledge.For example,if B←A,C←B,D←C,and E←D, KBANN generates a network with three hidden-layers in which concepts B,C,and D are represented.Obvi-ously,this creates a respective degradation in learning performance.Towell and Shavlik have tried to over-come this problem with a symbolic pre-processor of rules for KBANN[44].However,it introduces anoth-er preliminary phase to their translation process.16In our opinion the problem lies in KBANN’s translation algorithm,and can be straightforwardly solved by an accurate translation mechanism.
Summarizing,the experiments described above sug-gest that C-IL2P’s effectiveness is a result of three of the system’s features:C-IL2P is based on backpropa-gation,it uses background knowledge,and it provides an accurate and compact translation from symbolic knowledge to neural networks.
5.Future Work and Conclusion
There are some interesting open questions relating to the explanation capability of neural networks,speci?-cally to the trade-off between the complexity and qual-ity of rules’extraction methods.One way to reduce this trade-off would be to investigate more ef?cient prun-ing methods for neural networks’input vectors search spaces[19].
Another interesting question relates to the class of extended programs[45],that is of interest in connec-tion with the relation between Logic Programming and nonmonotonic formalisms.Extended logic programs, which add“classical”negation to the language of gen-eral programs,can be viewed as a fragment of De-fault theories[46].Commonsense knowledge can be represented more easily when“classical”negation is available.We have extended C-IL2P to deal with ex-tended logic programs.The extended C-IL2P system computes answer sets[45]instead of stable models. As a result it can be applied in a broader range of do-mains theories.The extended C-IL2P has already been applied in power systems’fault diagnosis,obtaining promising preliminary results[47].
By changing the de?nition of T P,variants of Default Logic and Logic Programming semantics can be ob-tained[48],de?ning a family of nonmonotonic(para-consistent)neural reasoning systems.Another inter-esting direction to pursue would be the use of labelled clauses in the style of[49],whereby the proof of a literal is recorded in the label.The learning and generaliza-tion capabilities of the network must also be formally studied,so paying due regard to the logical foundations of the system.The system’s extension to deal with?rst order logic is another complex and vast area for further research[50,60].
As a massively parallel nonmonotonic learning system,C-IL2P has interesting implications for the problem of Belief Revision[51](see also[52]).In the splice-junction determination problem,part of the background knowledge was effectively encoded as de-feasible,so that contradictory examples were able to specify a revision of the knowledge.Speci?cally,the knowledge regarding the Conformation of the genes has been changed.Hence,the background knowledge together with the set of examples can be inconsistent, and one needs to investigate ways to detect and treat inconsistencies in the system,viewing the learning pro-cess as a process of revision.
In this paper,we have presented the Connectionist Inductive Learning and Logic Programming System (C-IL2P);a massively parallel computational model based on arti?cial neural networks that integrates inductive learning from examples and background knowledge,with deductive learning from Logic Pro-gramming.We have obtained successful experimental results when applying C-IL2P to two real-world prob-lems in the domain of molecular biology.Both kinds of Intelligent Computational Systems,Symbolic and Connectionist,have virtues and de?ciencies.Research into the integration of the two has important implica-tions[53],in that one is able to bene?t from the advan-tages that each confers.We believe that our approach contributes to this area of research. Acknowledgments
We are grateful to Dov Gabbay,Valmir Barbosa, Luis Alfredo Carvalho,Alberto Souza,Luis Lamb, Nelson Hallack,Romulo Menezes and Rodrigo Basilio for useful discussions.We would especially like to thank Krysia Broda and Sanjay Modgil for their
Connectionist Inductive Learning and Logic75 comments.This research was partially supported by
CNPq and CAPES/Brazil.This work is part of the
project ICOM/ProTem.
Notes
1.An acceptable program P has exactly one stable model.
2.An interpretation is a function from propositional variables to
{“true”,“false”}.A model for P is an interpretation that maps P
to“true”.
3.The“suf?ciently small”restrictions are given by equations
μA max≤1
2and k A max≤12,respectively,where A max>0[21].
4.Note that a sound translation from P to N does not require all
the weights in N to have the same absolute value.We unify the weights(|W|)for the sake of simplicity of the translation algorithm and to comply with previous work.
5.It is known that an increase in the number of hidden layers in a
neural network results in a corresponding degradation in learning performance.
6.The recurrent connections represent an external process between
output and input.
7.The perturbation should be small enough not to have any effects
on the computation of the background knowledge.
8.These are the same problems that were investigated in[16]for the
evaluation of KBANN.We have followed as much as possible the methodology used by Towell and Shavlik,and we have used the same background knowledge and set of examples as KBANN.
9.Prokaryotes are single-celled organisms that do not have a nu-
cleus,e.g.,E.Coli.
10.Rules obtained from[16],and derived from the biological liter-
ature[54]from Noordewier[55].
11.Unlike prokaryotic cells,eukaryotic cells contain a nucleus,and
so are higher up the evolutionary scale.
12.Rules obtained from[16]and derived from the biological litera-
ture from Noordewier[56].
13.Towell and Shavlik compare KBANN with other hybrid systems
[57]and[58],obtaining better results.
14.In accordance with[16],1000examples are randomly selected
from the3190examples set for each training phase.
15.The network can get stuck in local minima during learning,in-
stead of?nding the global minimum of its error function. 16.KBANN already contains a preliminary phase of rules hierar-
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Connectionist Inductive Learning and Logic
77Artur Garcez is a Ph.D.student in the Department of Computing at Imperial College,London,UK.He received his B.Eng.in Com-puting Engineering from the Ponti?cal Catholic University of Rio de Janeiro (PUC-RJ),Brazil,in 1993and his M.Sc.in Comput-ing and Systems’Engineering from the Federal University of Rio de Janeiro (COPPE/UFRJ),Brazil,in 1996.He also worked in the Latin-American Technology Institute of IBM-Brazil.His research inter-ests include Symbolic-Connectionist Integration,Machine Learning,Neural Networks,Belief Revision and Commonsense (Practical)
Reasoning.Gerson Zaverucha is an Associate Professor of Computer Science at the Federal University of Rio de Janeiro (COPPE/UFRJ),Brazil.He received his B.Eng.in Electrical Engineering from the Federal University of Bahia,Brazil,in 1981,his M.Eng.in Electric Power Engineering from the Rensselaer Polytechnic Institute of Troy,New York,USA,in 1982and his Ph.D.in Computing from Imperial College,London,UK,in 1990.His research interests include Con-nectionist First Order Theory Re?nement,Machine Learning,Hy-brid Systems,Inductive Logic Programming and Nonmonotonic
Reasoning.
第二章_语言是符号系统参考答案
第二章语言是符号系统参考答案 一、名词解释 1、符号:符号是一个社会全体成员共同约定用来表示某种意义的记号、标志。其包括形式 和意义两个方面的要素,是一定的形式和一定的内容的统一体,二者缺一不可。 2、符号的任意性:语言符号的音义联系并非是本质的,必然的,而是由社会成员共同约定 的,一种意义为什么要用这个声音形式,而不用那种声音形式,这中间没有什么道理可言,完全是偶然的、任意的。语言符号和客观事物之间也没有必然联系。 3、二层性:语言可以分为不同的层级单位,语言系统就是由音位、语素、词、词组、句子 等结构单位组成的一种层级体系。语言的底层是音位。音位是语言符号中最小的辨义单位,位于语言符号结构的最底层。上层是音义结合的符号与符号的序列。上层又分为三级,第一级是语素,第二级是词,第三级是是句子。语言结构就是由音位层和音义结合的符号序列层构成的装置,我们称之为语言结构的二层性。 4、组合关系:语言系统中的符号和符号组合起来的关系称为符号的组合关系.符号的组合 关系是有条件的.比方说“红”和“花”两个符号可以组成“红花”和“花红‘。它们在两个组合中的关系不同,整个组合的性质也不同。符号和符号的组合形成语言的结构。 5、聚合关系:在语言链条的某一环节上能够互相替换的符号具有某种相同的作用,它们自 然地聚集成群。它们彼此的关系称为聚合关系。比方拿“红花“这个符号的链条来说,能出现在‘红”这个位置上的有“蓝、白、紫、大、小、好、香……”,能出现在“花” 这个位置上的有“光、线、旗、脸蛋、眼睛、房子……”,这两组词各构成一个聚合。 二、填空 1、任何符号,都是由(形式)和(内容)两个方面构成的。 2、一个符号,如果没有(内容),就失去了存在的必要,如果没有(形式),我们就无法感 知,符号也就失去了存在的物质基础。 3、语言符号是(声音)和(意义)的统一体,声音是语言符号的(物质表现形式)。 4、用什么样的语音形式代表什么样的意义,完全是由使用这种语言的社会成员(共同约定 俗成的)。 5、语言符号具有(任意性)和(线条性)。 6、语言的底层是一套(音位),上层是符号和符号的(序列),可以分为若干级,第一级是 (语素),第二级是(词),第三级是由词构成的句子。 7、语言系统中的所有符号,即可以同别的符号组合,又可以被别的符号替换,符号之间的 这两种关系是(组合关系)和(聚合关系)。 8、(组合)关系是指构成线性序列的语言成分之间的结构关系,(聚合)关系是指在线性序 列的同一结构位置上不同结构单位的替换规则。(可当定义) 9、人类之所以具有语言而动物没有,是因为人类具有(抽象思维)能力和(发音)能力。 三、判断正误 1、任何一种符号,都是由内容和意义两个方面构成的。(×) 2、从本质上看,语言其实是一种符号系统。(√) 3、人类选择语音而不是色彩、手势作为语言符号的形式,是因为语音比较好听。(×) 4、语言符号具有任意性特点,就是说我们平时说话用什么样的语音代表什么样的意义是自 由的,不受任何约束的。(×) 5、语言符号的约定俗成是指语音形式和意义内容的结合是社会成员共同约定认同的。(√)
液压系统符号
液压系统符号
1)液压系统图形符号的构成要素 构成液压图形符号的要素有点、线、圆、半圆、三角形、正方形、长方形、囊形. ※点表示管路的连接点,表示两条管路或阀板内部流道是彼此相通的 ※实线表示主油路管路; ※虚线表示控制油管路; ※点划线所框的内部表示若干个阀装于一个集成块体上,或者表示组合阀,或者表示一些阀都装在泵上控制该台泵。 大圆加一个实心小三角形表示液压泵或液压马达(二者三角形方向相反),中圆表示测量仪表,小圆用来构成单向阀与旋转接头、机械铰链或滚轮的要素,半圆为限定旋转角度的液压马达或摆动液压缸的构成要素。 ※正方形是构成控制阀和辅助元件的要素,例如阀体、滤油器的体壳等。 ※长方形表示液压缸与阀等的体壳、缸的活塞以及某种控制方式等的组成要素。 ※半矩形表示油箱,囊形表示蓄能器及压力油箱等。 (2)液压图形的功能要素符号 表示功能要素的图形符号有三角形、直与斜的箭头、弧线箭头等。 实心三角形表示传压方向,并且表示所使用的工作介质为液体。泵、马达、液动阀及电液阀都有这种功能要素的实心三角形。 箭头表示液流流过的通路和方向,液压泵、液压马达、弹簧、比例电磁铁等上面加的箭头表示它们是可进行调节的。 弧线单、双向箭头表示电机液压泵液压马达的旋转方向,双向箭头表示它们可以正反转。其他如“W”表示弹簧,“”表示电气,“.L”表示封闭油口,“*”表示节流阻尼小孔等。 (3)其他符号 管路连接及管接头符号、机械控制件和控制方式符号、泵和马达图形符号、液压缸图形符号、各种控制阀(如压力阀、流量阀、方向阀等)图形符号、各种辅助元件的图形符号、检测器或指示器图形符号等。 常用液压图形符号 (1)液压泵、液压马达和液压缸 名称符号说明名称符号说明 液压泵液压 泵 一般 符号 双 作 用 缸 不可 调单 向缓 冲缸 详细符号
第二章 语言是符号系统
第二章语言是符号系统 一、填空 1,、说出的话语句子是无限的,但无限多的句子都是由有限的()和()组装起来的。 2、符号包含()和()两个方面,二者不可分离。 3、语言符号的意义是对它所指代的一类()的概括。 4、我们是通过()认识到“孔子是中国古代的思想家”这个()的。 5、语言的表达是对心里现实的() 6、心理现实是存在于()和()之间的人脑中的信息存在状态。 7、语言符号的()和(),是语言符号的基本性质。 8、语言符号二层性的一大特点是()层的最小单位一定大大少于()层的最小单位。 9、()和()是语言系统中的两种根本关系。 10、动物无法掌握人类的语言,从生活基础看是不具有()和( ). 二、判断正误 1、一种语言可说出的句子是无限的。 2、语言是一种社会规约,所以每个人说话是不自由的。 3、符号的形式和意义都具有一般性。 4、语言是丰富人的心理现实的重要途径。 5、心理现实是对客观现实的认知,与客观现实是等同的。 6、语言符号形式与意义的关联很大程度上受制于它所属的符号系统。 7、现实中的句子是无穷的,所以组合关系也是无限的。 8、音位和音位组合构成语素。 9、句子是比词组高一层级的符号单位,所以句子的长度一定大于词组。 10、“飞鸟”和“小鸟”具有同样的组合关系。 三、思考题 1、听到一个熟人在说话,就能判断出是谁在说话,这个过程是语言符号在传递信息吗?为什么? 2、语言符号如何增进人的认识客观现实的能力? 3、什么是语言符号的任意性?表现在哪些方面? 4、如何理解语言符号的线条性和符号系统的关系? 5、说明“张三”、“李四”、“请”、“周日”、“春游”可以构成哪些句子,从符号的系统性说明为什么同样的语言符号单位可以构成不同的话语,表达不同的意思? 6、举例说明什么是语言符号的聚合关系,为什么说符号的聚合和组合是相互依存的? 7汉字是否是具有二层性的符号,为什么? 第二章答案 一、填空 1、词、规则 2、形式、意义
认识折纸符号解读
认识折纸符号 武安市贺进镇沙洺中心小学曹扬 教学目标: 1.学生初步了解纸的造型艺术与折纸工艺。 2.认识折纸符号,学会折纸的方法与步骤。 3.培养学生的动手能力、审美能力,合作意识和环保意识。 教学内容分析: 折纸是起源于中国的一门民间艺术,如今已风靡全球,并深受人们的喜爱,通过对本课的学习与了解,使学生了解和掌握折纸这种利用纸张装饰制作的特点及其特有的工艺和手工制作技艺,达到表现美、创造美、应用美,不仅愉悦自己身心,更具使环境和社会更加美好、和谐的目的。 学情分析: 折纸对于每个孩子来说都不陌生,从小到大同学们玩过折纸游戏,如纸飞机、纸船等,也掌握了一些简单的折纸技法,本课在学生具备了一定的折纸技法基础上,进行折叠纸的尝试练习,让学生对折纸造型有一个全新的认识。 教学重难点: 重点:折纸基本方法和步骤的掌握。 难点:运用折纸方法折出各种形状。 教学方法:引导法讨论法比较法讲授法。 教学准备:教具:实物投影,课件,各种示范作品。 学具:各色彩纸、剪刀、胶棒。 教学过程: 一、激趣导入 在一个晴朗的周末,兔妈妈带着兔宝宝到森林里采蘑菇,突然来了个大灰狼,大灰狼已经两天没吃东西了,饿极了,躲在大树下远远的张望着兔子们,口水都要流出来了。大灰狼悄悄地跟在小兔子后面,准备吃掉所有的小兔子,兔妈妈看到了后面的影子,大喊:“孩子们,快跑!”比比看,谁跑的快。小兔子就在我们这里,比比看谁跑的快。你想不想也用纸折的方法折出一只跑得快的小兔子呢?这节课,我们就来学习折纸。板书课题:折纸二、初识折纸符号 1.为了让同学们学会折纸的方法,老师给大家请来了三位新朋友帮忙。 大屏幕指示进展→ 凹折线(虚线)---------------- 凸折线(点画线)—-―-―-―- 教师介绍: 凹折线,要按照线的位置向里折。凸折线,要按照线的位置向外折。(动手制作考大家)老师还编了两句小儿歌,课件出示:凹折线,一边长,折在里面看不见;凸折线,长短变,折到外面来见面。(大屏幕考大家沿虚线往里夹折沿虚线朝外翻折)刚才那样把边和边对折叫对边折,那么把角和角对折叫什么呢?对角折。所有的折纸都是从单纯的对角折或
液压系统常用符号
液压系统常用符号 (3)压力控制阀 名称符号 说 明 名称符号 说 明 溢流阀 溢流阀 一 般符号 或直动 型溢流 阀 减 压阀 先导型比 例电磁式溢流 减压阀 先导型溢 流阀 定比减压 阀 压 比 1 / 3 先导型电 磁溢流阀 ( 常闭) 定差减压 阀 直动式比 例溢流阀 顺 序阀 顺序阀 般 符 号 或 睦 动 型 顺 序 阀 先导比例 溢流阀 先导型顺 序阀 卸荷溢流 阀 p2> p1时卸荷 单向顺序 阀(平衡阀)
双向溢流阀 直动式,外部泄油 卸荷阀 卸荷阀 般符号 或直动型卸荷阀 减 压 阀 减压阀 一般符号 或直动型减压阀 先导型电磁卸荷阀 1 >p 2 先导型减压阀 制动阀 双溢流制动阀 溢流减压阀 溢流油桥制动阀 (4)方向控制阀 名称 符号 说明 名称 符号 明 单向阀 单向阀 详细符号 换向阀 二位五通液动阀 简化符号(弹簧可省略) 二位四通机动阀 液压 单向 阀 液控单向阀 详 细符号(控制压力关闭阀) 三位四通电磁阀
简化符号 三位四通 电液阀 化 符 号 ( 内 控 外 泄 ) 详 细符号(控制压力打开阀) 三位六通手动阀 简 化符号(弹簧可省略) 三位五通电磁阀 双液控单向阀 三位四通 电液阀 控 内 泄 ( 带 手 动 应 急 控 制 装 置 ) 梭阀或门型 详 细符号 三位四通 比例阀 流 型 , 中 位 正 遮 盖
简化符号 三位四通 比例阀 位 负 遮 盖 换向阀 二位二通 电磁阀 常 断 二位四通 比例阀 常 通 四通伺服 二位三通 电磁阀 四通电液 伺服阀 级 二位三通 电磁球阀 电 反 馈 三 级 二位四通 电磁阀 (5)流量控制阀 名称符号 说 明 名称符号 明 节流阀可调节流 阀 详 细符号 调 速阀 调速阀 化 符 号 简 化符号 旁通型调 速阀 化 符 号 不可调节 流阀 一 般符号 温度补偿 型调速阀 化 符 号
如何认识常见的液压元件符号解读
如何认识常见的液压元件符号 液压系统的图形符号,各国都有不同的绘制规定。有的采用结构示意图的方法表示,称为结构式原理图。这种图形的优点是直观性强,容易理解液压元件的内部结构和便于分析系统中所产生的故障。但图形比较复杂,尤其是当系统的元件较多时,绘制很不方便,所以在一般情况下都不采用。有的采用原理性的只能式符号示意图,这种图形的优点是简单清晰,容易绘制。我国制定的液压系统图图形符号标准就是采用原理性的职能式符号绘制的。现将一些常见的液压元件职能式图形符号分类摘编于书后附表一中,并对阅读要点作如下简介: (1)油泵及油马达以圆圈表示。圆圈中的三角形表示液流方向,如果三角形尖端向外,说明液流向外输出,表示这是油泵。若三角形尖端向内,则说明液流向内输入,表示这是油马达。如果圆圈内有两个三角形,表示能够换向。若元件加一斜向直线箭头、则是可变量的符号,表示其排量和压力是可调节的。 (2)方向阀的工作位置均以方框表示。方框的数目表示滑阀中的位置数目,方框外的直线数表示液流的通路数,方框内的向上表示液流连同方向,“T”表示液流被堵死不通。方框的两端表示控制方式,由于控制方式不同,其图形符号也是不一样。 (3)压力阀类一般都是用液流压力与弹簧力相平衡,来控制液压系统中油液的工作压力。方框中的箭头数表示滑阀中的通道数,通道的连通分常开与常闭两种,在液压系统中科根据工作需要进行选择。 (4)节流阀通常以一个方框中两小段圆弧夹一条带箭头的中心直线表示。如果节流阀作用可调,则再在方框内画一条带箭头的斜线。 (5)将液压元件的图形符号有机地连接起来,即可组成一个完整的液压系统图(又称液压回路图)。
液压系统符号
1)液压系统图形符号的构成要素 构成液压图形符号的要素有点、线、圆、半圆、三角形、正方形、长方形、囊形、 ※点表示管路的连接点,表示两条管路或阀板内部流道就是彼此相通的 ※实线表示主油路管路; ※虚线表示控制油管路; ※点划线所框的内部表示若干个阀装于一个集成块体上,或者表示组合阀,或者表示一些阀都装在泵上控制该台泵。 大圆加一个实心小三角形表示液压泵或液压马达(二者三角形方向相反),中圆表示测量仪表,小圆用来构成单向阀与旋转接头、机械铰链或滚轮的要素,半圆为限定旋转角度的液压马达或摆动液压缸的构成要素。 ※正方形就是构成控制阀与辅助元件的要素,例如阀体、滤油器的体壳等。 ※长方形表示液压缸与阀等的体壳、缸的活塞以及某种控制方式等的组成要素。 ※半矩形表示油箱,囊形表示蓄能器及压力油箱等。 (2)液压图形的功能要素符号 表示功能要素的图形符号有三角形、直与斜的箭头、弧线箭头等。 实心三角形表示传压方向,并且表示所使用的工作介质为液体。泵、马达、液动阀及电液阀都有这种功能要素的实心三角形。 箭头表示液流流过的通路与方向,液压泵、液压马达、弹簧、比例电磁铁等上面加的箭头表示它们就是可进行调节的。 弧线单、双向箭头表示电机液压泵液压马达的旋转方向,双向箭头表示它们可以正反转。其她如“W”表示弹簧,“”表示电气,“.L”表示封闭油口,“*”表示节流阻尼小孔等。 (3)其她符号
管路连接及管接头符号、机械控制件与控制方式符号、泵与马达图形符号、液压缸图形符号、各种控制阀(如压力阀、流量阀、方向阀等)图形符号、各种辅助元件的图形符号、检测器或指示器图形符号等。 常用液压图形符号 (摘自GB/T786、1-1993) (1)液压泵、液压马达与液压缸 名称符号说明名称符号说明 液压泵液压泵 一般 符号 双 作 用 缸 不可 调单 向缓 冲缸 详细符号 单向定 量液压 泵 单向 旋转、 单向 流动、 定排 量 简化符号 双向定 量液压 泵 双向 旋转, 双向 流动, 定排 量 可调 单向 缓冲 缸 详细符号
认识符号=、>和<
认识符号=、>和<教案 教学内容: 苏教版国标教材一年级上册,第五单元:认数(一)认识=、>和<(教科书第18~19页的例题,第19页“想想做做”的习题。 教学目标: 1、初步学会用一一对应的方法比较物体的多少,了解“同样多”、“多”、“少”的含义。认识=、>和<表示的意思及用法,会比较5以内数的大小。 2、发展初步的观察能力、思维能力和语言表达能力。 3、使学生初步体会到生活中处处有数学,能积极参与数学学习的活动,喜爱学习数学。 教学重点:学会用=、>和<表示两数关系 教学难点:学会用>和<表示两数关系 教学具准备:教师准备教学课件、小动物头像、学生准备学具盒。 教材分析: 这节课主要教学符号=、>和<的认识,并应用这些符号表示两个数的大小关系。在学生的脑海中,大小比较已经有了一个初步的表象,只是还没有形成一个完整、系统的概念,更没有符号感。根据低年级学生的年龄特点,教材以“森林运动会”为情境的主题图引入,唤起学生的学生兴趣,并通过观察图,教师引导,让学生自己从不同动物只数的比较中,应用一一对应的方法,抽象出数的大小关系,从而体现学生在课堂上的主体地位;最后,让学生举出生活中的事例,把数学知识与生活实际联系起来,既运用了新知解决实际问题,又体现了新《课标》精神。 教学思想: 在教学中,我创设了各种有利于学生自主探索的学习情境,提供了学生自由选择比较方法的机会,鼓励学生在活动中养成独立思考、自主探索、不刻意模仿的思维方式。学生通过参与比较活动,逐步培养仔细观察、认真思考、学会倾听的学习习惯,同时还重视学生学习获取知识的过程引导和评价,此外还在活动中锻炼学生的语言表达能力及初步的推理能力。 整个教学过程,我注重运用新课改的理念转变自己在课堂中的地位,真正成为学生的组织者、引导者,以及学生学习的参与和合作伙伴,不仅关注学生知识的掌握和能力的发展,更对重视学生情感、态度、价值观的培养。 教学过程: 一、创设情境 1、导入:小朋友们,你们喜欢小动物吗?有一天,森林里热闹了起来,许许多多的小动物聚到了一起,原来呀——盼望已久的“森林运动会”开幕了。(出示主题图) 2、提问:瞧,有哪些队的小动物参加了比赛?每队的队员有多少呢? (学生数一数汇报)小朋友们,从这幅图中你还能知道些什么呢?(松鼠队队员最多;小熊队队员最少;小兔队和小猴队队员同样多。)
液压系统符号资料
常用液压图形符号 表1常用液压图形符号(摘自GB/T786.1-1993) (1)液压泵、液压马达和液压缸 名称符号说明名称符号说明 液压泵液压泵 一般符 号 双作 用缸 不可调 单向缓 冲缸 详细符号 单向定 量液压 泵 单向旋 转、单向 流动、定 排量 简化符号 双向定 量液压 泵 双向旋 转,双向 流动,定 排量 可调单 向缓冲 缸 详细符号 单向变 量液压 泵 单向旋 转,单向 流动,变 排量 简化符号 双向变 量液压 泵 双向旋 转,双向 流动,变 排量 不可调 双向缓 冲缸 详细符号 液压马达液压马 达 一般符 号 简化符号 单向定 量液压 马达 单向流 动,单向 旋转可调双 向缓冲 缸 详细符号 双向定 量液压 马达 双向流 动,双向 旋转,定 排量 简化符号 单向变 量液压 马达 单向流 动,单向 旋转,变 排量 伸缩缸
双向变量液压马达 双向流 动,双向 旋转,变 排量 压力 转换 器 气-液 转换器 单程作用 摆动马达 双向摆 动,定角 度 连续作用 泵-马达定量液 压泵- 马达 单向流 动,单向 旋转,定 排量 增压器 单程作用 变量液 压泵- 马达 双向流 动,双向 旋转,变 排量,外 部泄油 连续作用 液压整 体式传 动装置 单向旋 转,变排 量泵,定 排量马 达 蓄能 器 蓄能器一般符号 单作用缸单活塞 杆缸 详细符 号 气体隔 离式 简化符 号 重锤式 单活塞 杆缸 (带弹 簧复 位) 详细符 号 弹簧式 简化符 号 辅助气瓶柱塞缸气罐
伸缩缸 能量 源 液压源 一般符号 双作用缸 单活塞杆缸 详细符 号 气压源 一般符号 简化符号 电动机 双活塞杆缸 详细符号 原动机 电动机除外 简化符号 (2)机械控制装置和控制方法 名称 符号 说明 名称 符号 说明 机械控制件 直线运动的杆 箭头可省略 先导 压力控制方法 液压先导加压控制 内部压力控制 旋转运动的轴 箭头可省略 液压先导加压控制 外部压力控制 定位装置 液压二 级先导加压控制 内部压力控制,内部泄油 锁定装置 *为开锁的控制方法 气-液先导加压控制 气压外部控制,液压内部控制,外部泄 油 弹跳机构 电-液 先导加压控制 液压外部控制,内部泄油 机械控制方法 顶杆式 液压先导卸压控制 内部压力控 制,内部泄油 可变行 程控制式 外部压力控制(带遥控泄放 口)
语言是符号系统
第一章语言是符号系统 第一节语言符号的性质和特点 一、什么是符号 1.符号的含义:符号就是由一定的形式构成的表示一定意义的记号或标记,包括形式和意义两个方面,其作用是指称现实现象。符号是人们用来指代某种事物的标记。 2.符号的构成:符号是由形式和意义两个部分构成的结合体。形式就是符号外在的形状、结构,它是以某种物质的方式存在的,或者是声音,或者是线条,或者是色彩,等等。意义就是符号所代表的具体内容,任何一个符号,都有一定的意义。形式和意义在一定的符号系统中是密不可分的统一体,不能割裂二者之间的关系。 二、语言符号及其特点 1.语言符号:从本质上看,语言也是一种符号,也有形式和意义两个方面,具有符号的一切特点。语言符号是由声音形式和意义内容构成,语言符号是音与义相结合的统一体,是说的和听的。语言符号的音义结合是社会约定俗成的。同一般符号一样,用什么样的声音形式代表什么样的一样,是使用语言的社会集团的成员共同约定的,大家共同遵守。 2.语言符号又不同于一般符号。可从以下几个方面认识理解,首先,语言符号是声音和意义的结合体,是说的和听的;其次,一般符号的构成比较简单,而语言符号却是非常复杂的,可分不同的层级;再次,一般符号由于构造简单,因而只能表达有限的内容,而且这种内容是简单而固定的,语言符号则可以表达丰富多彩的意义;最后,语言符号具有以少驭多的生成机制,具有生成新的结构的能力,一般符号表达的意义是固定的,因而不能生成新的意义。 3.语言符号的特点:语言符号具有任意性和线条性的特点,其中重点理解任意性特点。 (1)所谓任意性,是指语言的声音形式和意义内容之间的联系是任意的,由社会约定俗成的,没有必然的、本质的联系。什么样的声音表达什么样的意义,什么样的意义由什么样的声音表达,是由社会全体成员共同约定并共同遵守的。为什么人类会有这样多形形色色的语言?这也只能从语言符号的声音和意义联系的任意性角度来解释。 (2)语言符号的任意性特点具体表现:要求结合人类不同的语言的种种现象来认识,有以下几个方面:第一,语言符号的音义的结合是任意性的,由社会约定俗成的,即什么样的语音形式表达什么样的意义内容,什么样的意义内容用什么样的语音形式表现是任意的;第二,不同语言有不同的音义联系,音义结合具有不同的特点;第三,同样的语音形式,在不同的语言中表示不同的意义,不同语言音义联系不对等;第四,同一语言的音义关系也有任意性,如方言。 (3)所谓线条性,是指语言符号在交际使用过程中,其声音形式只能一个一个依次出现,一个音素或一个音节发出来才能紧接着发出另一个音素或音节,形成线条,随着时间的推移而延伸,在时间的线条上绵延,不能同时在空间范围内展开。 第二节语言符号的系统性 一、语言的层级体系 语言中的各种单位相互间紧密联系,彼此依存,组成一个系统。语言的底层是音位,上层是音义结合的符号与符号的序列。上层又分为三级,第一级是语素,第二级是词,第三级是句子。 语言结构就是由音位层和音义结合的符号序列层构成的装置,我们称之为语言结构的二层性(定义-核心-展开)。无论是底层还是上层,我们都能看到,语言结构二层性的核心实际上是以少驭多,以较少的单位组成上一层较多的单位,如语言结构上层的三级在数量上就是增量翻番,由下一级组成上一级,数量成倍扩大。例如汉语,音位层只有30来个,它们可以组成数千个语素,语素又可以组成数十万条词,词又组成无穷无尽的句子。 语言就是各个单位在不同的层和级上构成的一个层级装置。在这个层级装置中,各个层级呈现出这样的特点,那就是层次的单位数量逐层增多,从有限到无穷。最底层的音位系统,一般只有几十个,它们为语言符号准备了形式的部分,音位组合与意义联系构成数千个语素,语素组合构成数万个词,数万个词再组成无数的句子。在这些组合中,从音位到语素是最关键的,因为音位还只是形式,结合成语素后与意义联系起来,从而产生了质的飞跃。 二、组合关系和聚合关系 有三个方面,一是掌握定义,二是举例分析,三是认识其作用。 1.组合关系:就是两个同一性质的结构单位(例如音位与音位、词与词等等),按照线性的顺序组合起来的关系。组合关系就是符号与符号组合起来的关系,也就是在一个结构中的词与词之间的关系。所以组合关系直接体现了语言的结构规
语言学概论-语言是符号系统练习(1)
语言学概论-语言是符号系统练习(1)
语言是符号系统练习 一、名词解释 1.符号 2.符号的任意性 3.语言符号 4.语言的层级体系 5.组合关系 6.聚合关系 7.语言能力 二、填空题 1.任何符号,都是由和两个方面构成的。2.语言符号是和的统一体,声音是语言符号的。 3.用什么样的语音形式代表什么样的意义,完全是由使用这种语言的社会成员。 4.语言符号具有和特点。 5.语言的底层是一套,上层是和,可以分为若干级,第一级是,第二级是,第三级是。 6.语言系统中的所有符号,既可以同别的符号组合,又可以被别的符号替换,符号之间的这两种关系是和。
7.人类之所以具有语言而动物没有,是因为人类具有能力和能力。 三、判断题 1.任何一种符号,都是由内容和意义两个方面构成的。() 2.从本质上看,语言其实是一种符号系统。()3.语言符号具有任意性特点,就是说我们平时说话用什么样的语音代表什么样的意义是自由的,不受任何约束。() 4.语言符号可以拆卸拼装,重复使用。() 5.语言符号是由大大小小的单位按一定规则构成的。() 6.通过符号的任意性特点,我们可以解释为什么人类社会有各种各样的语言。() 7.语言系统是由不同层级构成的,低一级的单位少,组成高一级后数量翻番增加。() 8.鹦鹉、八哥都会说话,有的甚至还会背古诗,可见,动物也有语言能力。() 9.语言是人类和动物相区别的标志之一。()10.动物掌握“语言”是先天的本领,人类掌握语言则需要后天的学习。() 四、问答题
1.举例说明什么是符号、符号是由哪些因素构成的?2.为什么说语言是一种符号系统? 3.人类选择语音作为语言的形式,同其它形式相比,语音形式有什么优点? 4.为什么说语言符号的形式和意义之间没有本质联系? 5.同样表示“父亲”、“母亲”,汉语用“bàba”、“mām ɑ”表示,英语用father、mother表示。为什么会有这样的差别呢? 6.为什么人类会有如此多样的语言? 7.为什么说语言符号在所有符号中是最重要、最复杂的一种? 8.语言符号的系统性表现在哪些方面? 9.举例什么什么是组合关系,什么是聚合关系。10.人类的语言能力主要表现在哪些方面? 11.人类语言和动物的“语言”的本质区别是什么? 一、名词解释 1.符号 符号,就是指代某种事物的标记,记号,它是由一个社
语言学概论 语言是符号系统练习(1)
语言是符号系统练习 一、名词解释 1.符号 2.符号的任意性 3.语言符号 4.语言的层级体系 5.组合关系 6.聚合关系 7.语言能力 二、填空题 1.任何符号,都是由和两个方面构成的。 2.语言符号是和的统一体,声音是语言符号的。 3.用什么样的语音形式代表什么样的意义,完全是由使用这种语言的社会成员。 4.语言符号具有和特点。 5.语言的底层是一套,上层是和,可以分为若干级,第一级是,第二级是,第三级是。 6.语言系统中的所有符号,既可以同别的符号组合,又可以被别的符号替换,符号之间的这两种关系是和。 7.人类之所以具有语言而动物没有,是因为人类具有能力和能力。 三、判断题 1.任何一种符号,都是由内容和意义两个方面构成的。() 2.从本质上看,语言其实是一种符号系统。() 3.语言符号具有任意性特点,就是说我们平时说话用什么样的语音代表什么样的意义是自由的,不受任何约束。() 4.语言符号可以拆卸拼装,重复使用。() 5.语言符号是由大大小小的单位按一定规则构成的。() 6.通过符号的任意性特点,我们可以解释为什么人类社会有各种各样的语言。() 7.语言系统是由不同层级构成的,低一级的单位少,组成高一级后数量翻番增加。() 8.鹦鹉、八哥都会说话,有的甚至还会背古诗,可见,动物也有语言能力。() 9.语言是人类和动物相区别的标志之一。() 10.动物掌握“语言”是先天的本领,人类掌握语言则需要后天的学习。() 四、问答题 1.举例说明什么是符号、符号是由哪些因素构成的? 2.为什么说语言是一种符号系统? 3.人类选择语音作为语言的形式,同其它形式相比,语音形式有什么优点? 4.为什么说语言符号的形式和意义之间没有本质联系? 5.同样表示“父亲”、“母亲”,汉语用“bàba”、“māmɑ”表示,英语用father、mother表示。为什么会有这样的差别呢? 6.为什么人类会有如此多样的语言? 7.为什么说语言符号在所有符号中是最重要、最复杂的一种? 8.语言符号的系统性表现在哪些方面? 9.举例什么什么是组合关系,什么是聚合关系。 10.人类的语言能力主要表现在哪些方面?
常用的液压图形符号
常用液压图形符号 (1)液压泵、液压马达和液压缸 名称符号说明名称符号说明 液压泵液压泵 一般符 号 双作 用缸 不可调 单向缓 冲缸 详细符号 单向定 量液压 泵 单向旋 转、单向 流动、定 排量 简化符号 双向定 量液压 泵 双向旋 转,双向 流动,定 排量 可调单 向缓冲 缸 详细符号 单向变 量液压 泵 单向旋 转,单向 流动,变 排量 简化符号 双向变 量液压 泵 双向旋 转,双向 流动,变 排量 不可调 双向缓 冲缸 详细符号 液压马达液压马 达 一般符 号 简化符号 单向定 量液压 马达 单向流 动,单向 旋转可调双 向缓冲 缸 详细符号 双向定 量液压 马达 双向流 动,双向 旋转,定 排量 简化符号 单向变 量液压 马达 单向流 动,单向 旋转,变 排量 伸缩缸
双向变量液压马达 双向流 动,双向 旋转,变 排量 压力 转换 器 气-液 转换器 单程作用 摆动马达 双向摆 动,定角 度 连续作用 泵-马达定量液 压泵- 马达 单向流 动,单向 旋转,定 排量 增压器 单程作用 变量液 压泵- 马达 双向流 动,双向 旋转,变 排量,外 部泄油 连续作用 液压整 体式传 动装置 单向旋 转,变排 量泵,定 排量马 达 蓄能 器 蓄能器一般符号 单作用缸单活塞 杆缸 详细符 号 气体隔 离式 简化符 号 重锤式 单活塞 杆缸 (带弹 簧复 位) 详细符 号 弹簧式 简化符 号 辅助气瓶柱塞缸气罐
伸缩缸 能量 源 液压源 一般符号 双作用缸 单活塞杆缸 详细符 号 气压源 一般符号 简化符号 电动机 双活塞杆缸 详细符号 原动机 电动机除外 简化符号 (2)机械控制装置和控制方法 名称 符号 说明 名称 符号 说明 机械控制件 直线运动的杆 箭头可省略 先导 压力控制方法 液压先导加压控制 内部压力控制 旋转运动的轴 箭头可省略 液压先导加压控制 外部压力控制 定位装置 液压二 级先导加压控制 内部压力控制,内部泄油 锁定装置 *为开锁的控制方法 气-液先导加压控制 气压外部控制,液压内部控制,外部泄 油 弹跳机构 电-液 先导加压控制 液压外部控制,内部泄油 机械控制方法 顶杆式 液压先导卸压控制 内部压力控 制,内部泄油 可变行 程控制式 外部压力控制(带遥控泄放 口)
懂液压图形符号、懂液压系统图
一瞧懂液压图形符号二瞧懂液压系统图 (1)液压系统图形符号得构成要素 构成液压图形符号得要素有点、线、圆、半圆、三角形、正方形、长方形、囊形 ●点表示管路得连接点,表示两条管路或阀板内部流道就是彼此相通得 ●实线表示主油路管路; ●虚线表示控制油管路; ●点划线所框得内部表示若干个阀装于一个集成块体上,或者表示组合阀,或者表示一些阀都装在泵上控制该台泵。 ●大圆加一个实心小三角形表示液压泵或液压马达(二者三角形方向相反),中●圆表示测量仪表,小圆用来构成单向阀与旋转接头、机械铰链或滚轮得要素,●半圆为限定旋转角度得液压马达或摆动液压缸得构成要素。 ●正方形就是构成控制阀与辅助元件得要素,例如阀体、滤油器得体壳等。 ●长方形表示液压缸与阀等得体壳、缸得活塞以及某种控制方式等得组成要素。 ●半矩形表示油箱,囊形表示蓄能器及压力油箱等。 (2)液压图形得功能要素符号 表示功能要素得图形符号有三角形、直与斜得箭头、弧线箭头等。 实心三角形表示传压方向,并且表示所使用得工作介质为液体。泵、马达、液动阀及电液阀都有这种功能要素得实心三角形。 箭头表示液流流过得通路与方向,液压泵、液压马达、弹簧、比例电磁铁等上面加得箭头表示它们就是可进行调节得。 弧线单、双向箭头表示电机液压泵液压马达得旋转方向,双向箭头表示它们可以正反转。其她如“W”表示弹簧,“”表示电气,“.L”表示封闭油口,“*”表示节流阻尼小孔等。 (3)其她符号 管路连接及管接头符号、机械控制件与控制方式符号、泵与马达图形符号、液压缸图形符号、各种控制阀(如压力阀、流量阀、方向阀等)图形符号、各种辅
助元件得图形符号、检测器或指示器图形符号将在本手册后续得相应内容中分别予以介绍,此处仅举出它们 得一些例子,如图1—4所示。
认识符号与标志
(苏教版二年级上册语文练习6) 认识标志与符号 ——实验小学金雯 教学目标: 1.认识一些气象符号,学习表示某些天气的字词。 2.认识标志,弄清标志所表示的意思。 3. 进行口语交际训练,提高学生的口语表达能力。 教学重难点: 认识气象符号、认识一些标志、口语交际。 教学过程: 一、认识标志 1、导入:我们的校园里有很多各种各样的标志,老师拍了几个,我们来看看。(出示标志,学生回答标志表示的意思) 师:圆圈加斜杠表示什么意思?(不可以这样做) 2、认识新标志 过渡:你看看,标志牌多方便呀!我们看到了标志,就知道了它想表达的意思。 ⑴出示新标志:你知道这些标志的意思吗?(第一个举手回答,后面抢答) ⑵这些标志会出现在哪些地方呢?(举手回答) 3、交流搜集的标志牌 (1)生活中的标志可多了,请大家把课前搜集的标志牌拿出来,和你的同桌交流交流。 (2)把你的标志牌展示给大家看看,说一说你画的标志牌是什么意思。
二、认识气象符号 1、过渡:标志给大家的生活带来了便利,还有一些符号也与大家的生活息息相关。 2、师:下面,老师播放一段天气预报的视频,请大家留心一下观察视频里的气象符号。 (播放视频) 指名回答:你看到了哪些气象符号? 3、(师出示练习图)这是我们常见的一些气象符号,你认识它们吗? ①先来认一认它们的名字。 自己在下面拼一拼、读一读。 指名读、齐读。 ②(下发题目)连线配对题。 ③反馈答案,再读名字。 4、思考: (1)“霜、雾、雷、雪”为什么都是雨字头呢? (2)你还知道哪些雨字头的字?(下发填空题) 5、小小气象员 (1)班级成立了“小小气象站”,我们大家都是“小小气象员”。这是我们的气象记录表。(展示记录表,认识表的内容。) (2)下面请大家看一则天气预报,记录下明天扬州地区的天气与温度。 (下发记录表,播放视频) (3)反馈
《认识数学符号》教案
备课纸 授课教师一年级数思学科16 年9月日 教学准备: 师:PPT、多媒体、日字格磁铁、直尺 教学过程: 教学过程: 一、导入(2分钟) 在数学世界中,许多图形的排列是有规律的,我们可以通过前面的几个图形的个数、形状、位置等方面进行观察,发现其中的规律,从而正确地画出来未知的图形。 二、新课(35分钟) (一)认识“+”、“-”、“=”、“>”、“<” <一>激趣导入 1、谁会数数,能大胆的站起来,数给大家听一听。 2、数得真好。数数中的这些数字是数学王国中的重要成员之一。其实在我们的数学王国里,还住着一些美丽的数学符号,你知道吗?(学生说不出来,老师可以引导,比如“+”) <二>认识数学符号 (一)出示5个数学符号 1、指名学生说自己知道的数学符号,只要回答正确就及时给予肯定。 2、学生没说到的老师补上。 数学王国里还有这些数学符号呢。老师随即出示数学符号。 3、老师领读一遍。 (二)知道符号的用途 1、加号的用途 师:我们通常在哪会看到加号? 生:在加法算式里。
师:比如在这道算式里(老师贴放算式卡片) 师:“+”表示两个数相加的一种运算 老师出示卡片:1+2=3 把1和2加起来,或者说合起来等于3。在加法算式中要用到加号。 2、减号的用途 师:我们通常在哪会看到减号? 生:在减法算式里。 师:比如在这道算式里(老师贴放算式卡片) 师:“-”表示从一个数里减去另一个数的一种运算 老师出示卡片:4-2=2 从4里面减去两个,或者说拿走两个,还剩2。在减法算式中要用到减号。 【小结】 “+”、“-”的出现有一个共同点,都是在算式里,因此这两个符号是运算符号。 师:这两个符号是什么符号?(运算符号) 3、“>”、“<”的用途 师:仔细观察一下,这两个符号有什么相同点和不同点?(长得一样,就是开口的方向不一样) 师:这两个符号用在比较大小里面。比如:(老师出示两张卡片)6>2、8<10 老师带领学生读一读 师:再请你仔细地观察一下,大于号和小于号在比较大小时开口有着怎样的特点?(开口对大数) 学习儿歌: 大于号,小于号,开口对着大数笑。 师:想一想,我们能不能用手势表示这个符号呢? 引出表示的方法: 大于号:伸出右手的食指和中指,将两指分开。 小于号:伸出左手的食指和中指,将两指分开。 玩手势游戏,进行巩固练习。老师报符号,学生做手势。 4、“=”的用途 老师先出示卡片,请学生观察。 师:观察等号两边的数字,你有什么发现?(一样大) 师:“=”表示两个数相等 【小结】 “>”、“<”、“=”通常在我们比较大小时用到它们,所以,这三个符号表示两个数量间的大
认识四声符号
认识四声符号: 一声平,二声扬三声拐弯,四声降。 一声高高平又平, 二声好像上山坡, 三声下坡又上坡, 四声就像下山坡。 汽车平走ā ā ā,汽车上坡aaa,汽车下坡又上坡ǎ ǎ ǎ,汽车下坡aaa。 小乌鸦它姓乌(wū),小胖子他姓吴(wú),一二三四五(wǔ),山上起了雾(wù)。 标调歌: 好朋友,排排坐,你挨着我来我挨着你。 a 在前边,i在后,挨在一起ai ai ai, 好朋友啊不分离。 有a在,把帽戴, a不在,o e戴, 要是i u在一起, 谁在后面给谁戴。 有a 找a,没a找o e ,i u 并排标后边 a o e i u ü六兄弟,老大在家找老大。 老大不在找老二,老二不在找老三,i u并列标在后。 a o e i u ü, 有a 在把花戴, a 字不在顺次排,i u 并列在一起,谁在后面给谁戴. 拼音休息操 点点头,伸伸腰,我们来做拼音休息操。 动动手,动动脚,我们来背声母表 b p m f ...... 扭扭脖子,扭扭腰,我们来背韵母表a o e ..... 站要直,坐要正,我们来背整体音节认读表zhi chi .... 专心听,勤动脑,学好拼音基础牢。 小鼓敲ddd;马蹄响dadada; 汽车开dididi;敲门声dududu。
小门小门n、n、n,小棍小棍l、l、l。 今天学习了n和l,小伙伴们真快乐。 单韵母a o e i u ü 圆脸小姑娘,小辫右边扎aaa 一只大公鸡,清早喔喔啼ooo 一只大白鹅,游在水中央eee 圆脸蛋羊角辫,张大嘴巴aaa 大公鸡,ooo。天天早起伸长脖。 小圆圈,圆又圆,它和小球差不多。 清清池塘一只鹅,水中倒影像个e 小嘴张开aaa 圆圆嘴巴ooo 扁扁嘴巴eee 短短蜡烛iii 小小茶杯uuu 鲤鱼吐泡üüü 公鸡打鸣,嘴巴圆圆o o o 。 白鹅照镜,嘴巴扁扁e e e 。 妈妈晾衣,上下对齐i i i. 。 乌鸦衔食,嘴巴小小u u u 。 小鱼吹泡,嘴巴扁扁üüü。 小i头上有点子 u是乌鸦鸟窝子 ü是小鱼吐泡子 牙齿对齐iii 嘴巴突出uuu 像吹笛子üüü 小i小i戴小帽,小u就象乌鸦巢,小ü爱哭把泪掉。整体认读音节yi 、wu、yu 小ü很骄傲,
常用液压图形符号
液压系统的组成 一个完整的液压系统由五个部分组成,即动力元件、执行元件、控制元件、辅助元件(附件)和液压油。动力元件的作用是将原动机的机械能转换成液体的压力能,指液压系统中的油泵,它向整个液压系统提供动力。液压泵的结构形式一般有齿轮泵、叶片泵和柱塞泵。执行元件(如液压缸和液压马达)的作用是将液体的压力能转换为机械能,驱动负载作直线往复运动或回转运动。控制元件(即各种液压阀)在液压系统中控制和调节液体的压力、流量和方向。根据控制功能的不同,液压阀可分为压力控制阀、流量控制阀和方向控制阀。压力控制阀又分为益流阀(安全阀)、减压阀、顺序阀、压力继电器等;流量控制阀包括节流阀、调整阀、分流集流阀等;方向控制阀包括单向阀、液控单向阀、梭阀、换向阀等。根据控制方式不同,液压阀可分为开关式控制阀、定值控制阀和比例控制阀。辅助元件包括油箱、滤油器、油管及管接头、密封圈、快换接头、高压球阀、胶管总成、测压接头、压力表、油位油温计等。液压油是液压系统中传递能量的工作介质,有各种矿物油、乳化液和合成型液压油等几大类。 常用液压图形符号 液压泵一般符号 单向定量液压单向 转、单向 动、定排 双向定量液压双向 转,双向 动,定排 单向变量液压单向 转,单向
双向变量液压双向 转,双向 动,变排 液压马一般 符号 单向定量液压单向 动,单向旋转 双向定量液压双向 动,双向 转,定排 单向变量液压单向 动,单向 转,变排 双向变量液压双向 动,双向 转,变排
摆动马双向 动,定角 定量液单向 动,单向 转,定排 变量液双向 动,双向 转,变排量,外部泄油 液压整体式传动装置单向 转,变排 泵,定排量马 单活塞详细符号 简化符号
幼儿园中班认知各种各样的标志
教学资料参考范本 幼儿园中班认知各种各样的标志 撰写人:__________________ 部门:__________________ 时间:__________________
认知——各种各样的标志 一.活动由来:目前,安全工作是幼儿园工作中的重中之重,为了更好的落实××区教委关于安全系列教育活动的通知精神,真正 做到安全教育融入幼儿日常教育活动中。 二.活动目标: 1. 丰富幼儿安全常识,提高幼儿自我保护能力。 2. 引导幼儿认识生活中常见的标志,知道它们的名称和作用。 3. 鼓励幼儿完整语句讲述。 三.活动准备:1.指导幼儿简单时装表演,不要让幼儿知道 2.各种标志图 四.活动过程: 1. 时装表演,吸引幼儿注意力。 师:“今天,咱们班的四位小朋友给大家准备了一个小节目。” 2. 巩固认知图形,鼓励幼儿大胆完整的讲述。 师:“表演结束了,我的问题也来了——你认为小演员表演的怎 么样?哪儿比较好(衣着,动作),还有一个非常特别之处你们发现 了吗?(衣服上帖了标志图) 师:“那现在咱们将这些标志介绍一下好吗?(这是什么标志,表示什么意思?) 3. 认识新标志 师:“告诉我,这上面画着什么?表示什么意思?” “为什么要设立这些标志?标志有什么作用?”
4. 小结:标志可以告诉人们在这里可以做什么,不可以做什么?人人都应该遵守标志的要求。 5. 延伸:(1)你还在哪儿见过什么样的标志?它表示什么意思? (2)你认为咱们班那些地方需要设立什么样的标志? 五.活动 反思: 本次活动,教师以时装表演的形式引出主题,很新颖,很好的调动了幼儿的学习兴趣。最初认识标志,待幼儿能力稍强时可以启发鼓励幼儿自己制作标志,帖在班内,幼儿园内,不仅能丰富幼儿的安全知识,提高自我保护能力,真正使安全教育溶入日常教育活动中。