模糊控制 英文文献
CONTROL, PID CONTROL, AND
ADVANCED FUZZY CONTROL
FOR SIMULATING A NUCLEAR
REACTOR OPERATION
XIAOZHONG LI and DA RUAN*
elgian Nuclear Research Centre (SCKoCEN
Boeretang 200, 8-2400 Mol, Belgium
(Received 15 March 1999)
Based on the background of fuzzy control applications to the first nuclear reactor in Belgium (BRI) at the Belgian Nuclear Research Centre (SCK.CEN), we have made a real fuzzy logic control demo model. The demo model is suitable for us to test and com- pare some new algorithms of fuzzy control and intelligent systems, which is
advantageous because it is always difficult and time-consuming, due to safety aspects, to do all experiments in a real nuclear environment. In this paper, we first report briefly on the construction of the demo model, and then introduce the results of a fuzzy control,
a proportional-integral-derivative (PID) control and an advanced fuzzy control, in which
the advanced fuzzy control is a fuzzy control with an adaptive function that can
Self-regulate the fuzzy control rules. Afterwards, we present a comparative study of those
three methods. The results have shown that fuzzy control has more advantages in terms
of flexibility, robustness, and easily updated facilities with respect to the PID control of
the demo model, but that PID control has much higher regulation resolution due to its integration term. The adaptive fuzzy control can dynamically adjust the rule base,
therefore it is more robust and suitable to those very uncertain occasions.
Keywords: Fuzzy control; PID control; fuzzy adaptive control; nuclear reactor
I INTRODUCTION
Today the techniques of fuzzy logic control are very mature in most
engineering areas, but not in nuclear engineering, though some research has been done (Bernard, 1988; Hah and Lee, 1994; Lin et al. 1997; Matsuoka, 1990). The main reason is that it is impossible to do experiments in nuclear engineering as easily as in other industrial areas. For example, a reactor is usually not available to any individual. Even for specialists in nuclear engineering, an official licence for doing any on-line test is necessary. That is why we are still
conducting projects such as "fuzzy logic control application" in BRl (the first nuclear reactor in Belgium) (Li and Ruan, 1997a; Ruan, 1995; Ruan and Li, 1997; 1998; Ruan and van der Wal, 1998). In the framework of this project, we find that although there are already many fuzzy logic control applications, it is difficult to select the most sui-
table for testing and comparison of our algorithms. Moreover, due to the safety regulations of the nuclear reactor, it is not realistic to perform many experiments in BRl. In this situation, we have to conduct part of the pre-processing experiments outside the reactor, e.g., com-
parisons of different methods and the preliminary choices of the parameters. One solution is to make a simulation programme in a computer, but this has the disadvantage that in which, however, the real time property cannot be well reflected. Therefore another solution has adopted, that is, we designed and made a water-level
control system, referred to as the demo model, which is suitable for our testing and experiments. In particular, this demo model (Fig. 1) is designed to simulate the power control principle of BRl (Li et al., 1996a,b; Li and Ruan, 1997b).
In this demo model, our goal was to control the water level in tower TI at a desired level by means
of tuning VL (the valve for large control tower T2) and VS (the valve for small control tower T3). The pump keeps on working to supply water to T2 and T3. All taps are for manual tuning at this time. VI and V2 valves are used to control the water levels in T2 and T3 respectively. For example, when the water level in T2 is lower than photoelectric switch sensor 1 then the on-off valve V, will be opened (on), and when the water level in T2 is higher than photoelectric switch sensor 2 then the on-off valve Vl will be closed (off). The same is true of V2. Only when both VI and V2 are closed V3 will be opened, because it can decrease the pressure of the pump and thereby prolong its working life. The pressure sensor is used to detect the height of water level in TI. So for TI, it is a dynamic system with two entrances and one exit for water flow.
COMPARATIVE STUDY OF FUZZY CONTROL
The Demo Model Structure
FIGURE 1 The working principle of the demo model.
BRI is a 42-year old research reactor, in which the control method is the simple on-off method. Many methods called traditional meth- ods, when compared to fuzzy logic, are still very new to the BR1 reactor. One of these, proportional-integral-derivative (PID) control, has to be tested as well as fuzzy logic method. So far, we have tested the normal fuzzy control, traditional PID control, and an advanced fuzzy control on this demo model. To obtain a better demonstration, these three approaches have been programmed and integrated into one con- roller system based on the programmable logic controller (PLC) of the OMRON company. The purpose of tlus paper is to report comparative experimental results of these three methods from the demo model. Section 2 simply introduces a normal fuzzy control and its result.
Section 3 introduces a PID control and its result.
Section 4 introduces an advanced fuzzy control which is able to self-regulate the Fuzzy control rules. Section 5 compares the previous three methods and their results.
2 FUZZY CONTROL
The fuzzy control algorithm in this demo model is a normal algorithm based on the Mamdani model. To simulate the BRl reactor, we use two fuzzy controllers (FLCl and FLC2) to control VL and VS separately (note: it is possible to use one fuzzy logic controller with two outputs to control VL
and VS and the related result can be referred to (Li and Ruan, 1997b)). Let D be the difference between the actual value (P) of water level and the set value (S) and DD be the derivative of D, in other words, the speed and direction of the change of water level. VL and VS represent the control signal to VL (Iarge valve) and VS (small valve), respectively. When D is too big, we use FLC1 to control VL (main-tuning); When D is small, we use FLC2 to control VS
(fine-tuning). We choose D and DD as inputs of the fuzzy logic con- troller, and VL or VS as the output of the fuzzy logic controller. D and DD must be fuzzified before fuzzy inference. Suppose the universes of discourse (or input variables' intervals) of D and DD are -d, dj and [-dd,dd], respectively. We use 7 fuzzy sets to partition hem, i.e., Negative Large (NL), Negative Middle (NM), Negative Small (NS), Zero (ZE), Positive Small (PS), Positive Middle (PM), and Positive Large (PL). As for VL and VS, because the result of fuzzy reasoning is also a fuzzy linguistic value, the universes of discourse of VL and VS also need to be fuzzified. We use those 7 fuzzy linguistic erms too. Symmetrical trianglar-shaped functions are used to define the membership functions for input variables (Li et al., 1995; 1996a,b), and singletons are for output variables (Ornron, 1992). Each fuzzy controller has one rule base which contains 49 fuzzy control rules. The its rule can be represented as the following form: if D is Ai and DD is Bi, then VL (or VS) is Ci where A, Bi, and Ci are fuzzy linguistical values, such as NL, PS, and so on. The above rule is sometimes abbreviated as (Ai, Bi : Ci). Figure 2 shows a control effect of a synthetic control process. It first goes up from 0 to 20cm then keeps on at 20 an, next drops down from 20 to 10 cm and finally keeps on at 10 cm.
In view of this figure, we know that the fuzzy control has quick responses (quickly approaching the set value) and small overshoot (almost invisible), but with a small steady error (not so smooth in a steady state).
COMPARATWE STUDY OF FUZZY CONTROL
FIGURE 2 The control effect of fuzzy control to the demo model.
3 PID CONTROL
In the PID control, it is difficult to control VL and VS separately like the previous fuzzy control with a good control result, because the integration term of the PID control needs some time, and this will result in an oscillation when switching control signal between VL and VS. From this point of view the PID control is worse than the fuzzy control. Therefore, in our tests, VL and VS have to be controlled by the same signal. We use the following formula:
dt
de
d i p
e T dt T e
K U(t)++=?
By substitution,
dt de
d i
e pe K dt K K U(t)++=?
where U(I): control value to VL and VS at time r; e: the set value-the real value at time I; Kp: the proportional parameter and Kp = (1IPB) x loo%, where PB is the proportional band; Ki: the integration
FlGURE 3 The trajectory of the water level by the PID control.
parameter and Ki = l/Ti where Ti is the integration time; Kd: the differential parameter and Kd = Td where Td is the differential time. In practice, a discrete form of the above formula is used
)]1()([)](....)2()1([K e(t)K U(t)i P --+
+++=t e t e T K t e e e T s
d s wher
e T, is the sample period. Figure 3 shows a result o
f the PID control,where PB= l5%, Ti=30s, Td= 10s. In view of this figure, the PID control is very stable (very smooth in steady states), and has quick responses too, but with visible overshoots.
4 ADVANCED FUZZY CONTROL
The kernel part of the fuzzy logic control is the fuzzy rule base with linguistic terms, though the membership functions and scale factors also have an important effect on the fuzzy logic controller. There are some papers which discuss how to adjust membership functions and/or scale factors (Batur and Kasparian, 1991; Chou and Lu, 1994; Tonshoff and Walter, 1994; Zheng, 1992). This section focuses on rules. Normally the methods of deriving rules can be broadly divided into two types, sourceable and non-sourceable. The sourceable method means the rules are obtained from some information source, such as human experience or historical input-output data. Experience has been widely used by the fuzzy engineers, especially by the early fuzzy engineers. The problem of using human experience is that it is time-consuming, and to some degree subjective. In order to overcome these problems, particularly avoiding the subjectivity, historical input -output data-if available can be used. To obtain rules from such data, many methods are used, one of the popular approaches is neural net- works (NN) (Berenji and Khedkar, 1992; Halgamuge and Glesner, 1994; Jang, 1992; Kosko, 1992; Li et al., 1995; Lin ez al., 1995; Takagi
and Hayashi, 1991; Wang and Mendel, 1992). One problem of the sourceable method is that it depends strictly on the source which will be transformed into rules. In the case that the source is noisy, then the rules might be biased. Another problem of the sourceable method is that it is usually non-adaptive, i.e., all the rules are fixed, therefore it cannot perform well under a dynamic environment.
The non-source- able methods are source-free and they produce and choose rules according to a performance measurement of the controller, such as
genetic algorithms (GA) (Karr, 1991; Lim et al., 1996; Qi and Chin, 1997) (mostly also generating membership functions and scale factors) and self-organizing controllers (SOC) (He er al., 1993; Li et al., 1996a,b; Lin et al., 1997, Procyk and Mamdani, 1979; Shao, 1988; Tanscheit
and Scharf, 1988; Wu et al., 1992). With GA it is possible to find integratedly optimal parameters but GA is very computation rich, and furthermore, it is almost impossible to apply GA in a real complex system without a simulation model. Perhaps the SOC is the only method which has the following advantages: objective, adaptive, less computation required, more error-tolerant, and simple.
FIGURE 4 An adaptive function is incorporated into a fuzzy control system.
The general principle of the SOC is that the controller monitors its own performance and adjusts its control rules to improve performance for time-varying and unknown plants. The problem of the SOC show to perform the performance measurement. The basic way is to design a performance measurement table which looks like a fuzzy control rule table and to use it to assess the performance of the controller rules) (Procyk and Mamdani, 1979), but to design such a performance measurement table is also very difficult (Chung and Oh, 1993) and it is system-dependent. Based on the SOC, this section will introduce an adaptive method which uses a set of new norms to replace the ormer performance measurement. The new norms are very simple
and system-independent, therefore they can be easily applied to most fuzzy controllers. In this section, the advanced fuzzy control means the above SOC, in other words, a fuzzy control with an adaptive function, where the adaptive function contains two steps: performance judgement and changing fuzzy control rules. Figure 4 illustrates how an adaptive function is incorporated into the fuzzy control system. At the beginning of each cycle, the controller's last behaviour is judged and then the rule base is changed accordingly. In this cycle, the controller will use the new rule base and output the result to the controlled object. The behaviour of the new rule base will be judged and changed again in the next cycle.
4.1 The Principle of the Adaptive Function
Let D and DD represent error (the difference between the actual value and the desired value) and change in error, respectively. Let D(t) and DD(t) represent error and change in error at time t, respectively. They are two input variables. Let U be an output variable, and assume thetotal number of the rules is n, then every rule has the following form: if D is A, DD is Bi, then U is C;, i= 1,2 ,..., n, where A, Bi, and Ci are fuzzy linguistic values and i is an index pointing out each rule's position in the rule table (or the rule data file). User[i] to represent the fuzzy control magnitude (conclusion fuzzy set) of the ith rule, and let simply
][
i r
7,6,5,4,3,2,1
where 1=NL,2=NM,3=NS,4=ZE,5=PS,6=PMY7=PL.
In general, a control locus may be expressed with Fig. 5, and it can be regarded as having up to four feature sections and four feature points. For each feature part, we offer a norm to guide the regulation of the fuzzy control rules. For example, the current water level P(t) is in the feature part (I), then after the fuzzy controlling using the current control rules, we measure the water level P(t + l) at the next time which has three possibilities:
P(l) < P(t + 1) P(t + 1) < S and P(t + 1) P(t+l)>S. FIGURE 5 Any trajectory has up to four feature sections and four feature points. The related norm to guide how to change rules is the following: (i) if D(I + 1) 5 0 and DD(t + I) > 0, that is, P(t) < P(t + 1) 5 S, then r[i] = r[i] (ii) if D(i f 1) < 0 and DD(t + 1) < 0, that is, P(t +1) < S and P(t + 1) < P(t), then r[i] = r[i] +a, (iii) if D(t + 1) > 0, that is, P(t + 1) > S, then r[i] = r[i] - a, where a is a step size and a = 1,2,3,4,5,6. In case (i), the fuzzy con- troller makes the water level P(t f 1) closer to the set value S, therefore the behaviour of the fuzzy controller is good, no rules should be changed; In case (ii), the fuzzy controller makes the water level P(t + 1) further from the set value S, therefore the behaviour of the fuzzy con- troller is not good, the strength is too weak and the action of the corresponding rules 'should be stronger; In case (iii), the fuzzy controller makes the water level P(t $1) overpass the set value S, therefore the behaviour of the fuzzy controller is not good, the strength is too strong and the action of the corresponding rules should be weakened. Not all rules but some of those that are activated in last cycle should be regulated. We use the following formula to describe which should be adjusted: ))()(A ()()(A j j i i DD B D DD B D i j C C ∧∨=∧= which means the ith rule is changed only if it is the largest activated among those activated rules which have the same conclusion part. For example, (NL, NM : PL) and (NM, NM : PL) are two activated rules and have the same conclusion part, i.e., PL. Comparing NL(D) A NM(DD) with NM(D) A NM(DD), the larger one corresponds to the rule which should be adjusted. 4.2 An Experimental Result To guarantee no overshoot, the best way is to initialize all rules as the same conclusion part: NL, as shown in Table I. In this table, for example, NL at the row 2 and column 3 means: if D is NM and DD is NL then VL or VS is NL. All rules have the same conclusion part though condition parts are different. Figure 6 illustrates TABLE I The initial rule table for both FLCl and FLC2 FIGURE 6 Comparison between adaptive fuzzy control and fuzzy control. the comparison result between fuzzy adaptive control and. fuzzy con- trol with the above rule base. In this example, the set value is 20cm. Both start from Ocm. During the first stage, i.e., increasing from zero, some analytic rules manipulate the valves and not fuzzy control. Only after the water level reaches 18cm does the fuzzy controllers start to operate VL and VS. Apparently, the adaptive fuzzy control has a much better result by self-regulating gradually fuzzy control rules. The nor- mal fuzzy control without adaptive function cannot self-regulate rules, therefore it cannot draw up the water level. About 10 min later, we observe the rule tables on the screen and find both rule tables have changed a lot. Table I1 gives the result of FLCl and Table 111 gives the result of FLC2, where the regulated rules are marked by bold fonts. 4.3 Some Remarks for the Adaptive Function The initial idea about the previously described norms of the adaptive function, which was published in (Li et al., 1996a,b), and where a simulated inverted pendulum system and a real industrial heating system were used to make testings, gave satisfactory results . a The parameter a is influential on the overshoot and response time (rise time). When cr is too big, there will be a large overshoot possibly; when a is too smaI1, possibly there will be a long response time (Li el al., 1996a,b). The adaptive function considers only the last value, that is, it uses P(r - 1) not P(t - T) (T is the delay) to decide P(t), but our experimental results show the effect is good, although the valves of the demo model have a maximum delay of 90 s. The adaptive function selects only some of the rules according to formula (1) for adjustment, not all activated rules like (Lin et al., 1997). This makes the transition of the rules more smooth, i.e., without or with less resonance. Selecting initial rules appropriately will benefit the control effect. For example, if the overshoot is strictly limited, we may initialize all rules with the conclusion part of NL, as was done in the previous experiment. Once some experience has been obtained, it can be transformed into the initial rules of the adaptive function, the advantage being that the rise time will be shorter (Li and Ruan, 1998). The rule, "if D is ZE and DD is ZE then U is ZE," should be fixed, and this will help the system to become stable. The adaptive function is very helpful in keeping the system stable in a steady state. It cannot guarantee no overshoot if the initial rules are randomly selected. The adaptive function cannot adjust membership functions and scale factors. 5 COMPARATIVE STUDY Each method has both advantages and disadvantages, the details of which are described in Table IV, where * is used to represent the degree of a property, and the more *, the higher the degree. For example, the realization of an adaptive fuzzy logic controller (FLC) is more difficult than a normal fuzzy controller, but a normal fuzzy controller is more difficult to realize than a PID controller. The PID control has the smallest static error and steady error. The dynamic regulation of TABLE IV Comparative study of FLC, PID and adaptive FLC the control rules in an adaptive controller can help in reducing the static error and steady error (Li et al., 1996a,b). As for robustness, it has been accepted that FLC is more robust than PID. Herein we also give one example, as shown as in Fig. 7. This experiment was carried out after tuning the Tap 1 (see Fig. 1) to make the outflow much smaller. We found that the reaction of FLC was better than that of PID, hough the FLC had a small static error. If we count the total number of * for each method, we will find that PID and FLC have the same score, 17. Adaptive FLC has a higher score of 20. This interesting result can be explained by the following facts. PID and FLC have their own strong points, and they compensate each other. Adaptive FLC adds an adaptive function to a normal FLC, therefore its score should be higher than that of FLC. A natural result is that combining FLC and PID should be better than each method alone. The comparative method above is perhaps a little subjective, but it does reflect some objective properties and relationships among those three methods. In the real world, one may use other ways to evaluate these methods. For example, if robustness is stressed, then it should be highly weighted when the total scores are calculated. 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So m e em pl oy t he spl i t pr og ra m/da ta m e mo ry o f th e H a rv ar d ar ch it ect u re , sh ow n in Fi g.3-5A -1, o th ers fo ll ow t he p h il os op hy , wi del y a da pt ed f or ge n er al -p ur po se co m pu te rs a nd m i cr op ro ce ss o r s, o f ma ki ng n o log i ca l di st in ct ion be tw ee n p r og ra m an d d at a m e mo ry a s i n t he P r in ce to n ar ch ite c tu re , sh ow n i n F ig.3-5A-2. In g en er al te r ms a s in gl e -chi p m ic ro co mp ut er i s c h ar ac te ri ze d b y t h e i nc or po ra ti on o f a ll t he un it s of a co mp uter i n to a s in gl e d ev i ce , as s ho wn in Fi g3-5A -3. Fig.3-5A-1 A Harvard type Program memory Data memory CPU Input& Output unit memory CPU Input& Output unit MIS(Management Information System) the term in the interest of the administration. In the wake of the development of MIS, much business sit up the decentralized message concentration to establish the information system ministry of directly under director, and the chief of information system ministry is ordinarily in the interest of assistant manager’s grade. After the authority of business is centralized up high-quality administration personnel staff’s hand, as if causing much sections office work decrease, hence someone prophesy, middle layer management shall vanish. In reality, the reappearance phase employed layer management among the information system queen not merely not to decrease, on the contrary there being the increase a bit. This is for, although the middle layer management personnel staff getting off exonerate out through loaded down with trivial details daily routine, yet needs them to analyses researching work in the way of even more energy, lift further admonishing the decision of strategic importance level. 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It completely on the basis of the user requirement organization design together with manufacture, may carry on the large-scale cooperation in the interest of identical produce by means of the business that the flow was shifted the distinct districts, and by means of the once more programming to the machinery with to the resources and the reorganization of personnel staff , constituted a fresh affrication system, and causes that manufacturing cost together with lot nearly have nothing to do with. Quickly manufacturing the business establishes a whole completely new strategy dependence relation against consumer, and is able to arouse the structure of production once more revolution. The management information system is towards the self-adoption and Self-learning orientation development, the decision procedure of imitation man who is be able to be better. Some entrepreneurs of the west vainly hope that consummate MIS is encircles the magic drug to govern the business all kinds of diseases; Yet also someone says, and what it is too many is dependent on the defeat that MIS be able to cause on the administration. It is adaptable each other to comprehend the effect to the business of MIS, and is favor of us to be living in development and the research work, and causes the business organization and administer the better development against MIS of system and administration means , and establish more valid MIS. 英文翻译文章的出处：Russ Basiura, Mike Batongbacal 管理信息系统: 管理信息系统就是我们常说的MIS(Management Information System), 在强调管理， 世界各地搞笑的十句蹩脚英语 ①If you want just condition of warm in your room, please control yourself。 日本旅馆：如果您想调节您房间的温度，请控制您自己。 ②Please don't feed the animals. If you have any food, please give it to the guard on duty。 匈牙利动物园：请不要给动物喂食。如果您有食品，请喂给值班警卫。 ③Ladies are requested not to have children in the bar。 挪威酒吧：女士们不要在酒吧里生孩子。 ④Fur coats made for ladies from their skins。 瑞典皮货商店：为女士们制作的皮大衣，是用她们的皮制成的。 ⑤Teeth extracted by the latest Methodists。 香港牙科诊所：由最新的卫理公会教徒给您拔牙。 ⑥Drop your trousers here for best results。 泰国的干洗店：在这里脱掉您的裤子，等待最好的结果。 ⑦Specialist in women and other diseases。 意大利妇科诊所：我们是women 和其他疾病的专家。 ⑧Welcome to the cemetery where famous Russian artists are buried daily except Thursday。 俄国公墓：欢迎访问这个公墓，许多著名的俄国艺术家每天埋在这里，但星期四不埋。 ⑨We take your bags and send them in all directions。 丹麦机场：我们将拿走您的行李，送往四面八方。 ⑩The manager has personally passed all water served here。 墨西哥旅馆：旅馆经理将亲自为您撒尿。 模糊理论综述 引言 模糊理论（Fuzzy Logic)是在美国加州大学伯克利分校电气工程系的L.A.zadeh（扎德）教授于1965年创立的模糊集合理论的数学基础上发展起来的,主要包括模糊集合理论、模糊逻辑、模糊推理和模糊控制等方面的内容.L.A.Zadeh教授在1965年发表了著名的论文，文中首次提出表达事物模糊性的重要概念：隶属函数，从而突破了19世纪末康托尔的经典集合理论,奠定模糊理论的基础。1974年英国的E.H.Mamdani成功地将模糊控制应用于锅炉和蒸汽机的控制，标志着模糊控制技术的诞生。随之几十年的发展，至今为止模糊理论已经非常成熟，主要包括模糊集合理论、模糊逻辑、模糊推理和模糊控制等方面的内容。 模糊理论是以模糊集合为基础，其基本精神是接受模糊性现象存在的事实，而以处理概念模糊不确定的事物为其研究目标，并积极的将其严密的量化成计算机可以处理的讯息，不主张用繁杂的数学分析即模型来解决问题。 二、模糊理论的一般原理 由于客观世界广泛存在的非定量化的特点，如拔地而起的大树，人们可以估计它很重，但无法测准它实际重量。又如一群人，男性女性是可明确划分的，但是谁是“老年人”谁又算“中年人”；谁个子高，谁不高都只能凭一时印象去论说，而实际人们对这些事物本身的判断是带有模糊性的，也就是非定量化特征。因此事物的模糊性往往是人类推理，认识客观世界时存在的现象。虽然利用数学手段甚至精确到小数点后几位，实际仍然是近似的。特别是对某一个即将运行的系统进行分析，设计时，系统越复杂，它的精确化能力越难以提高。当复杂性和精确化需求达到一定阈值时，这二者必将出现不相容性，这就是著名的“系统不相容原理”。由于系统影响因素众多，甚至某些因素限于人们认识方法，水准，角度不同而认识不足，原希望繁荣兴旺，最后导致失败，这些都是客观存在的。这些事物的现象，正反映了我们认识它们时存在模糊性。所以一味追求精确，倒可能是模糊的，而适当模糊以达到一定的精确倒是科学的，这就是模糊理论的一般原理。 三、模糊理论的分支 它可分类为模糊数学、模糊系统，模糊信息，模糊决策，模糊逻辑与人工智能这五个分支，它们并不是完全独立的，它们之间有紧密的联系。例如，模糊控制就会用到模糊数学和模糊逻辑中的概念。从实际应用的观点来看，模糊理论的应用大部分集中在模糊系统上，尤其集中在模糊控制上。也有一些模糊专家系统应用于医疗诊断和决策支持。 模糊逻辑：模糊逻辑指模仿人脑的不确定性概念判断、推理思维方式，对于模型未知或不能确定的描述系统，以及强非线性，大滞后的控制对象，应用模糊集合和模糊规则进行推理，表达过渡性界限或定型知识经验，模拟人脑方式，实行模糊综合判断，推理解决常规方法难于对付的规则型模糊信息问题。模糊逻辑善于表达界限不清晰的定性知识与经验。它借助于隶属度函数概念，区分模糊集合，处理模糊关系，模拟人脑实施规则型推理，解决因“排中律”的逻辑破缺产生的种种不确定问题。模糊逻辑是处理部分真实概念的布尔逻辑扩展。经典逻辑坚持所有事物（陈述）都可以用二元项（0或1，黑或白，是或否）来表达，而模糊逻辑用真实度替代了布尔真值。这些陈述表示实际上接近于日常人们的问题和语意陈述，因为“真实”和结果在多数时候是部分（非二元）的和/或不精确的（不准确的，不清晰的，模糊的）。真实度经常混淆于概率，但是它们在概念上是不一样的；模糊真值表示在模糊定义的集合中的成员归属关系，而不是某事件或条件的可能度（likelihood）。要展示这种区别，考虑下列情节: Bob在有两个毗邻的屋子的房子中:厨房和餐厅。在很多情况下，Bob的状态是在事物“在厨房中”的集合内是完全明确的:他要么“在厨房中”要么“不在厨房中”。但 西南交通大学 毕业设计（英文翻译） 节能型机房温湿度远程控制系统测控节点设 计 年级: 学号: 姓名: 专业: 指导老师: 二零一一年六月 Mosix Mosix修改bsdi上的BSD/ OS来提供电脑上的跨网络的动态负载平衡组和先发制人进程迁移。这是不错的东西，不只是为并行处理，但对于通常使用一个集群很像一个可扩展的SMP。是否有Linux版本？详细信息，请看www.cs.huji.ac.il/mosix/。NOW (工作站网络) 伯克利工作站网络项目，https://www.wendangku.net/doc/35719414.html,/，已经极推广了并行计算中使用工作站网络，这里还有很多工作在进行中，都致力于“在未来几年展示一个实际的100处理器系统”。唉，他们不使用linux。 并行处理使用linux 并行处理使用Linux WWW站点，https://www.wendangku.net/doc/35719414.html,/LDP/,是这些指南和许多相关文件包括为全日制量身订造的在线幻灯片的家园。除了在报纸项目上，普渡大学电机与计算机工程系已经是并行处理的领先者，这个的设立是为了帮助别人申请并行处理的Linux电脑。 自从普渡大学的第一组Linux个人电脑在1994年2月组装以来，已经诞生了许多的Linux PC集群，有些还包括视频墙。然而这些集群使用386，486和奔腾系统（没有奔腾Pro系统），英特尔最近获得普渡大学的捐赠，将允许它的奔腾II系统构建多个大型集群（与单个群集计划多达165机）。并且这些集群都将有论文网，也具有最传统的网络。 奔腾Pro集群研讨会 1997年，4月10-11日，埃姆斯实验室在得梅因，爱荷华州举行了奔腾Pro集群研讨会。WWW在本次研讨会，https://www.wendangku.net/doc/35719414.html,/workshops/PPCworkshop.html，从云集的参加者中收集了很多丰富的PC集群信息。 TreadMarks帝斯曼(分布式共享存) 帝斯曼（分布式共享存）是一种技术，即一个消息传递系统可以出现的行为作为一个SMP。有不少这样的系统，其部分使用OS页故障触发消息传输机制。TreadMarks，https://www.wendangku.net/doc/35719414.html,/~willy/TreadMarks/overview.html，是这种系统更有效地之一，并在Linux集群上运行。坏消息是“TreadMarks”正在由一个小成本的大学和非盈利机构的分配。欲了解更多有关该软件，请联系https://www.wendangku.net/doc/35719414.html,信息。 U型网 (用户级网络接口架构) U型网（用户级网络接口架构）在康奈尔大学， Managemengt Information Systems By a management information system,we propose the follow alternate definition: an integrated uer/machine system (usually computerized) for providing information to support decision making in an enterprise. The key elements of this definition are —An integrated uer/machine system —For proving information —To support decision making —In an enterprise A management information system utilizes —Computer hardware and software —Manual procedures —Models for analysis —A database Just as there is a logical flow of materials in the creation of a product, there is logical flow of information in a management information system.In manufacturing,raw materials move through a process that transforms the raw materials into usable products. In a similar fashion, in an information system,data are supplied to a system(input), the data are manipulated(processed),and they are transformed into information(output).In its simplest form ,a management information systemed may be depicted by an input-process-output(IPO) model 超级雷人的脑筋急转弯大全及答案 1. 一点一横长，一撇到南洋，上面像个丑，下面一张口。答案：唐. 2. 小明看书的时候，为什么不能把书签放在页和页之间?答案：因为是写在一张纸上. 3. 世界上哪个地方下午比早上先到?答案：在字典里. 4. 什么走路早上用四条腿,中午用两条腿,晚上用三条腿答案：人. 5. 什么车坐不了?答案：风车. 6. 什么酒喝不了?答案：天长地久 7. 如果苹果没落在牛顿头顶上，会落到哪里?答案：地上. 8. 钟和九点钟有什么不一样?答案：差一点. 洋洋不慎撞到电线杆，为什么他连手都疼?答案：洋洋狠狠的揍了电线杆一顿. 9. 有一样东西不管你喜欢与否，它却每年一定要增加一点，这是什么东西?答案：年龄. 10.动物园里，小明紧挨着老虎合影留念，老虎却没有咬他，为什么?答案：那是只假 老虎. 如果苹果没落在牛顿头顶上，会落到哪里?答案：地上. 1. 黄河上有座桥，一高一低，这座桥都被接连而来的次洪水淹没了。高桥被淹了次，低桥反只被淹了次，这是为什么?答案：水退后高桥露出来而低桥一直淹着. 2. 王爷爷有个孙子。一天，他买了个小西瓜，一路在想怎样平均分西瓜，总也想不 出个好办法来。在门口，邻居李奶奶只说了个字，王爷爷就愁眉舒展了。李奶奶告诉他的 是什么办法?答案：榨成汁. 3. 一根绳子在当中被一刀剪断了，但它仍是一根完整的绳子，为什么?答案：因为绳 子起初是结成圆形圈的. 4. 陆先生有两位朋友，都在税务局当税务官员，其中一位是另一位的孩子的父亲。 你说会有这种可能吗?答案：两位税务官员是夫妇. 5. 李伯伯一共有个儿子，这个儿子又各有一个妹妹，那么，李伯伯一共有几个子女? 答案：八个子女妹妹最小. 6. 在有个代表队参加的足球淘汰赛中，要决出冠军队，至少需进行多少次比赛?答案：一次只能淘汰一个队故需要次. 物理与电子工程学院 《人工智能》 课程设计报告 课题名称关于模糊控制理论的综述 专业自动化 班级 11级3班 学生姓名郑艳伟 学号 指导教师崔明月 成绩 2014年6月18日 关于模糊控制理论的综述 摘要：模糊控制方法是智能控制的重要组成部分，本文简要回顾了模糊控 制理论的发展，详细介绍了模糊控制理论的原理和模糊控制器的设计步骤， 分析了模糊控制理论的优缺点以及模糊控制需要完善或继续研究的内容，根 据各种模糊控制器的不同特点，对模糊控制在电力系统中的应用进行了分 类，并分析了各类模糊控制器的应用效能.最后，展望了模糊控制的发展趋 势与动态. 关键词：模糊控制；模糊控制理论；模糊控制系统；模糊控制理论的发展模糊控制是以模糊集理论、模糊语言变量和模糊控制逻辑推理为基础的一种智能控制方法，从行为上模拟人的思维方式，对难建模的对象实施模糊推理和决策的一种控制方法.模糊控制作为智能领域中最具有实际意义的一种控制方法，已经在工业控制领域、电力系统、家用电器自动化等领域中解决了很多的问题，引起了越来越多的工程技术人员的兴趣. 模糊控制系统简介 模糊控制系统是以模糊集合论、模糊语言变量和模糊逻辑推理为基础的一种计算机数字控制技术.1965年美国的扎德[1]创立了模糊集合论, 1973 年, 他给出了模糊逻辑控制的定义和相关的定理.1974 年英国的Mamdani 首先用模糊控制语句组成模糊控制器,并把它用于锅炉和蒸汽机的控制, 在实验室获得成功, 这一开拓性的工作标志着模糊控制论的诞生. 模糊控制系统主要是模拟人的思维、推理和判断的一种控制方法, 它将人的经验、常识等用自然语言的形式表达出来, 建立一种适用于计算机处理的输入输出过程模型, 是智能控制的一个重要研究领域.从信息技术的观点来看, 模糊控制是一种基于规则的专家系统.从控制系统技术的观点来看, 模糊控制是一种普遍的非线性特征域控制器. 相对传统控制, 包括经典控制理论与现代控制理论.模糊控制能避开对象的数学模型(如状态方程或传递函数等) , 它力图对人们关于某个控制问题的成功与失败和经验进行加工, 总结出知识, 从中提炼出控制规则, 用一系列多维模糊条件语句构造系统的模糊语言变量模型, 应用CRI 等各类模糊推理方法, 模糊逻辑与模糊控制技术的发展 宁廷群1 肖英辉1任惠英2 （1山东科技大学机电学院山东青岛 266510 2山东兖矿集团机械制修厂山东邹城 273500）The Development of Fuzzy Logic and Fuzzy Control Technology 摘要：针对现代工业控制领域的模糊控制技术的新发展，综合介绍了当代该领域的基本理论和发展现状，展望了未来的发展应用。 关键词：模糊控制；应用发展；自适应控制。 Abstract: This paper introduces the development of fuzzy logic and fuzzy control technology in modern control domain, and discusses the basic theory and main development in integration. At last it gives some prospects. Key words: fuzzy control, development and application, adaptive control 一、引言 在现代工业控制领域，伴随着计算机技术的突飞猛进，出现了智能控制的新趋势，即以机器模拟人类思维模式，采用推理、演绎和归纳等手段，进行生产控制，这就是人工智能。其中专家系统、模糊逻辑和神经网络是人工智能的几个重点研究热点。相对于专家系统，模糊逻辑属于计算数学的范畴，包含有遗传算法，混沌理论及线性理论等内容，它综合了操作人员的实践经验，具有设计简单，易于应用、抗干扰能力强、反应速度快、便于控制和自适应能力强等优点。近年来，在过程控制、建摸、估计、辩识、诊断、股市预测、农业生产和军事科学等领域得到了广泛应用。为深入开展模糊控制技术的研究应用，本文综合介绍了模糊控制技术的基本理论和发展状况，并对一些在电力电子领域的应用作了简单介绍。 二、模糊逻辑与模糊控制 １、模糊逻辑与模糊控制的概念 １９６５年，加州大学伯克利分校的计算机专家Ｌofty Ｚadeh提出“模糊逻辑”的概念，其根本在于区分布尔逻辑或清晰逻辑，用来定义那些含混不清，无法量化或精确化的问题，对于冯˙诺依曼开创的基于“真－假”推理机制，以及因此开创的电子电路和集成电路的布尔算法，模糊逻辑填补了特殊事物在取样分析方面的空白。在模糊逻辑为基础的模糊集合理论中，某特定事物具有特色集的隶属度，他可以在“是”和“非”之间的范围内取任何值。而模糊逻辑是合理的量化数学理论，是以数学基础为为根本去处理这些非统计不确定的不精确信息。 模糊控制是基于模糊逻辑描述的一个过程的控制算法。对于参数精确已知的数学模型，我们可以用Berd图或者Nyquist图来分析家其过程以获得精确的设计参数。而对一些复杂系统，如粒子反应，气象预报等设备，建立一个合理而精确的数学模型是非常困难的，对于电力传动中的变速矢量控制问题，尽管可以通过测量得知其模型，但对于多变量的且非线性变化，起精确控制也是非常困难的。而模糊控制技术仅依据与操作者的实践经验和直观推断，也依靠设计人员和研发人员的经验和知识积累，它不需要建立设备模型，因此基本上是自适应的，具有很强的鲁棒性。历经多年发展，已有许多成功应用模糊控制理论的案例，如Rutherford，Carter 和Ostergaard分别应用与冶金炉和热交换器的控制装置。 2、分析方法探讨 工业控制系统的稳定性是探讨问题的前提，由于难以对非线性和不统一的描述，做出判断，因此模糊控制系统的分析方法的稳定性分析一直是一个热点，综合近年来各位学者的发表的论文,目前系统稳定性分析有以下集中: 1、李普亚诺夫法:基于直接法的离散时间(D-T)和连续时间模糊控制的稳定性分析和设计方法,相对而言起稳定条件比价保守. 电厂蒸汽动力的基础和使用 1.1 为何需要了解蒸汽 对于目前为止最大的发电工业部门来说, 蒸汽动力是最为基础性的。 若没有蒸汽动力, 社会的样子将会变得和现在大为不同。我们将不得已的去依靠水力发电厂、风车、电池、太阳能蓄电池和燃料电池,这些方法只能为我们平日用电提供很小的一部分。 蒸汽是很重要的,产生和使用蒸汽的安全与效率取决于怎样控制和应用仪表,在术语中通常被简写成C&I(控制和仪表 。此书旨在在发电厂的工程规程和电子学、仪器仪表以 及控制工程之间架设一座桥梁。 作为开篇,我将在本章大体描述由水到蒸汽的形态变化,然后将叙述蒸汽产生和使用的基本原则的概述。这看似简单的课题实际上却极为复杂。这里, 我们有必要做一个概述:这本书不是内容详尽的论文,有的时候甚至会掩盖一些细节, 而这些细节将会使热力学家 和燃烧物理学家都为之一震。但我们应该了解,这本书的目的是为了使控制仪表工程师充 分理解这一课题,从而可以安全的处理实用控制系统设计、运作、维护等方面的问题。1.2沸腾:水到蒸汽的状态变化 当水被加热时,其温度变化能通过某种途径被察觉(例如用温度计 。通过这种方式 得到的热量因为在某时水开始沸腾时其效果可被察觉,因而被称为感热。 然而,我们还需要更深的了解。“沸腾”究竟是什么含义?在深入了解之前,我们必须考虑到物质的三种状态:固态,液态,气态。 (当气体中的原子被电离时所产生的等离子气体经常被认为是物质的第四种状态, 但在实际应用中, 只需考虑以上三种状态固态, 物质由分子通过分子间的吸引力紧紧地靠在一起。当物质吸收热量,分子的能量升级并且 使得分子之间的间隙增大。当越来越多的能量被吸收,这种效果就会加剧,粒子之间相互脱离。这种由固态到液态的状态变化通常被称之为熔化。 当液体吸收了更多的热量时,一些分子获得了足够多的能量而从表面脱离,这个过程 被称为蒸发(凭此洒在地面的水会逐渐的消失在蒸发的过程中,一些分子是在相当低的 温度下脱离的,然而随着温度的上升,分子更加迅速的脱离,并且在某一温度上液体内部 变得非常剧烈,大量的气泡向液体表面升起。在这时我们称液体开始沸腾。这个过程是变为蒸汽的过程,也就是液体处于汽化状态。 让我们试想大量的水装在一个敞开的容器内。液体表面的空气对液体施加了一定的压 力,随着液体温度的上升,便会有足够的能量使得表面的分子挣脱出去,水这时开始改变 自身的状态,变成蒸汽。在此条件下获得更多的热量将不会引起温度上的明显变化。所增 加的能量只是被用来改变液体的状态。它的效用不能用温度计测量出来,但是它仍然发生 着。正因为如此,它被称为是潜在的,而不是可认知的热量。使这一现象发生的温度被称为是沸点。在常温常压下,水的沸点为100摄氏度。 如果液体表面的压力上升, 需要更多的能量才可以使得水变为蒸汽的状态。 换句话说, 必须使得温度更高才可以使它沸腾。总而言之,如果大气压力比正常值升高百分之十,水必须被加热到一百零二度才可以使之沸腾。 本科毕业设计(论文)外文翻译 译文： ASP ASP介绍 你是否对静态HTML网页感到厌倦呢？你是否想要创建动态网页呢？你是否想 要你的网页能够数据库存储呢？如果你回答：“是”，ASP可能会帮你解决。在2002年5月，微软预计世界上的ASP开发者将超过80万。你可能会有一个疑问什么是ASP。不用着急，等你读完这些，你讲会知道ASP是什么，ASP如何工作以及它能为我们做 什么。你准备好了吗？让我们一起去了解ASP。 什么是ASP? ASP为动态服务器网页。微软在1996年12月推出动态服务器网页,版本是3.0。微软公司的正式定义为:“动态服务器网页是一个开放的、编辑自由的应用环境,你可以将HTML、脚本、可重用的元件来创建动态的以及强大的网络基础业务方案。动态服务器网页服务器端脚本,IIS能够以支持Jscript和VBScript。”(2)。换句话说,ASP是微软技术开发的,能使您可以通过脚本如VBScript Jscript的帮助创建动态网站。微软的网站服务器都支持ASP技术并且是免费的。如果你有Window NT4.0服务器安装,你可以下载IIS（互联网信息服务器）3.0或4.0。如果你正在使用的Windows2000,IIS 5.0是它的一个免费的组件。如果你是Windows95/98,你可以下载(个人网络服务器(PWS),这是比IIS小的一个版本，可以从Windows95/98CD中安装，你也可以从微软的网站上免费下载这些产品。 好了，您已经学会了什么是ASP技术，接下来，您将学习ASP文件。它和HTML文 件相同吗？让我们开始研究它吧。 什么是ASP文件？ 一个ASP文件和一个HTML文件非常相似，它包含文本，HTML标签以及脚本，这些都在服务器中，广泛用在ASP网页上的脚本语言有2种，分别是VBScript和Jscript，VBScript与Visual Basic非常相似，而Jscript是微软JavaScript的版本。尽管如此，VBScript是ASP默认的脚本语言。另外，这两种脚本语言，只要你安装了ActiveX脚本引擎，你可以使用任意一个，例如PerlScript。 HTML文件和ASP文件的不同点是ASP文件有“.Asp”扩展名。此外，HTML标签和ASP代码的脚本分隔符也不同。一个脚本分隔符,标志着一个单位的开始和结束。HTML标签以小于号(<)开始(>)结束，而ASP以<%开始，%>结束，两者之间是服务端脚本。 从“雷人”的英语翻译说起———谈英语语言的? ? 摘要：双关修辞是英语语言中常见的一种修辞格，大体可以分为语（谐）音双关、语义双关、词性双关和仿拟双关。英语双关修辞历史悠久，广泛运用在英 语各种文体中：文学作品、广告、谜语等，在我们周围无处不在。因此，要想 精准地翻译一篇英文，必须了解并熟悉双关修辞，且经过反复练习，吸收更广 泛的知识，才能战胜这一翻译的难点，越过这个可译性障碍，使译文与原文达 到最大限度的等值。关键词：修辞手法双关语翻译我在十几年的英语教学中， 深切地感受到学生对英语又敬又畏的心情，爱它可又驾驭不了它。于是学生在 与英语的焦灼对峙中，出现了一些雷人的英语翻译，让人叹为观止。1.How are you?How old are you?怎么是你，怎么老是你？2.We two who and who?咱俩谁跟 谁呀。3.You me you me.彼此彼此。4.You give me stop!你给我站住！5.Go past no mistake past.走过路过，不要错过。其实英语与汉语一样，也有多种修辞手法。其中双关运用就在英语语言中占有举足轻重的地位，如果我们对英语的修辞手法，尤其是双关运用不熟悉的话，就会出现更多雷人的语言。下面我将重点阐 述英语语言的双关运用。一、语（谐）音双关词语之间因拼写相似、发音相同 或相近而构成的双关用法。它是利用发音相同或相近但意义不同的词来代替所 要表达的本意，它会让语言变得风趣俏皮，增加感染力。1.There was a man in the restaurant.“You’re not eating your fish.”the waitress said to him， “Anything wrong with it?”“Long time no sea.”the man replied.其中“see”与“sea”发音相同，但意义完全不同。这个顾客的回答，表面上听“Long time no see.”是好久不见的意思，但实际上是说那些鱼离开大海很久了，已经不新 鲜了。2.What is the clearest animal?什么动物最聪明？The pigs that nose everything.什么都能闻的猪。nose是“闻”的意思，碰巧与knows（知道）谐音。如此一来，“什么都能闻的猪”就变成了“什么都知道的猪”，那当然是最聪 明的动物。3.What country has a good appetite?哪个国家的人胃口很好？Hungary. 匈牙利。因为“Hungary”这个国家单词的发音与“hungry”（饥饿）的发音相近，于是在这则笑话中，“Hungary”因语音双关而成为最能吃的国家。二、语义双关因为词语的多重含义而构成的双关用法。在字面上虽然只有一个词语， 而实际上却同时兼顾着两种不同的意义，言在此而意在彼，从而造成一种含蓄、深沉委婉、耐人寻味的意境，增强了语言的表达效果。1.Why is a river rich?Because it always has two banks.“bank”一词有两个含义，一个意思为“河岸”，另一个意思则表示“银行”。大家想想，有两个河岸（银行）的河流能 不富有吗？2.The professor rapped on his desk and shouted，“Gentlemen，order.”The entire class yelled“beer”.这则幽默同样用了双关手法中的同形异义词。 模糊控制研究及发展现状综述 模糊控制研究及发展现状综述 摘要：模糊控制是智能控制的重要组成部分。本文主要介绍了模糊控制理论的研究及发展的现状等 ,详细介绍了模糊控制理论的原理、模糊控制的数学基础, 其发展现状中介绍了模糊 PID 控制、自适应模糊控制、神经模糊控制、遗传算法优化的模糊控制、专家模糊控制等 , 还介绍了一些模糊控制的软硬件产品, 对模糊控制系统的稳定性作了简单介绍, 最后对模糊控制的发展作了展望。 关键词：模糊控制；模糊控制器 引言 模糊控制是近代控制理论中的一种基于语言规则与模糊推理的高级控制策略和新颖技术，它是智能控制的一个重要分支，发展迅速，应用广泛，实效显著，引人关注。随着科学技术的进步，现代工业过程日趋复杂，过程的严重非线性、不确定性、多变量、时滞、未建模动态和有界干扰，使得控制对象的精确数学模型难以建立，单一应用传统的控制理论和方法难以满足复杂控制系统的设计要求。而模糊控制则无需知道被控对象的精确数学模型，且模糊算法能够有效地利用专家所提供的模糊信息知识，处理那些定义不完善或难以精确建模的复杂过程。因此，模糊控制成为了近年来国内外控制界关注的热点研究领域。 模糊控制作为智能领域中最具有实际意义的一种控制方法 ,已经在工业控制领域、家用电器自动化领域和其他很多行业中解决了传统控制方法无法或者是难以解决的问题, 取得了令人瞩目的成效, 引起了越来越多的控制理论的研究人员和相关领域的广大工程技术人员的极大兴趣。 一：模糊控制简介 模糊控制是以模糊集合论、模糊语言变量和模糊逻辑推理为基础的一种计算机数字控制技术。 1965 年美国的扎德创立了模糊集合论, 1973 年, 他给出了模糊逻辑控制的定义和相关的定理。 1974 年英国的 Mamdani 首先用模糊控制语句组成模糊控制器,并把它用于锅炉和蒸汽机的控制, 在实验室获得成功, 这一开拓性的工作标志着模糊控制论的诞生。 模糊控制主要是模拟人的思维、推理和判断的一种控制方法, 它将人的经验、常识等用自然语言的形式表达出来, 建立一种适用于计算机处理的输入输出过程模型 , 是智能控制的一个重要研究领域。从信息技术的观点来看 , 模糊控制是一种基于规则的专家系统。从控制系统技术的观点来看, 模糊控制是一种普遍的非线性特征域控制器。 相对传统控制, 包括经典控制理论与现代控制理论。模糊控制能避开对象的数学模型 (如状态方程或传递函数等), 它力图对人们关于某个控制问题的成功与失败和经验进行加工 , 总结出知识 , 从中提炼出控制规则 , 用一系列多维模糊条件语句构造系统的模糊语言变量模型 , 应用 C RI 等各类模糊推理方法,可以得到适合控制要求的控制量, 可以说模糊控制是一种语言变量的控制. 模糊控制具有以下特点: 变频空调器的模糊控制技术 (陇东人作品) （XXX 能源学院陕西西安710054 ） 摘要：对变频空调器的模糊控制技术的原理作了研究，讨论了变频空调器模糊控制系统的特点。分析总结了国内变频空调器模糊控制技术的研究现状以及发展趋势，同时对变频空调器模糊控制技术未来的研究问题进行了展望。 关键词：变频空调器；模糊控制；展望 Developing Tendency and Current Situation of Fuzzy Control in In- verter Room Air Conditioner XXX (Xi'an XX,College of Energy Resources Engineering, Shaanxi, Xi'an710054, P.R.China) Abstract：In this paper, the fuzzy control technology is briefly introduced, and from different directions discusses the characteristics of fuzzy control system. Current domestic developing ten-dency and current situation of fuzzy control in inverter room air conditioner is summarized, while future research issues about the technology of fuzzy control in inverter room air conditioner were discussed. Keywords: inverter room air conditioner; fuzzy control; current situation; developing tendency; development 0引言 随着世界范围内能源危机的到来，各国政府都在为经济的可持续发展积极地推广节能降耗技术。作为家庭用电的主要设备，传统空调器由于其运行效率低下正在逐渐退出市场，而变频空调器（Inverter Room Air Conditioner，MAC）是制冷理论、热动力学、电机驱动技术、电力电子技术、微电子技术和智能控制理论交叉发展应用的产物，由于其高效节能和实现智能化控制的优异特性，使之成为家用空调器的主要发展方向。 变频空调器的空气调节效果虽然比传统定速空调器有所提高，但变频空调器容易控制、反应快、高效节能等特点并没有完全展现出来。智能控制方法的出现打破了传统控制的模型限制，将模糊控制技术应用于变频空调器中，使空调性能更为优越。可以说控制系统是整个变频空调器的心脏，研究变频空调器的控制技术，对变频空调器的节能运行至关重要。 鉴于变频空调器系统属于参数时变、非线性、大纯滞后系统的特点，所以采用具有学习功能的模糊控制方法，根据系统响应自动建立和修改控制规则，不断自动改善其性能，与传统的控制方法相比能达到较好控制效果。本文主要讨论变频空调器的模糊控制技术，以及该技术的现状和研究进展。 1变频空调器模糊控制技术 1.1 模糊控制电气工程及其自动化专业_外文文献_英文文献_外文翻译_plc方面
管理信息系统MIS(Management Information System)
趣味英语和雷人中式英语
模糊理论综述
自动化专业英文文献翻译2
管理信息系统中英文翻译资料
超级雷人的脑筋急转弯大全及答案
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