智能控制试卷及答案4套

1、附件1智能控制课程试题A题号-一一二三四五六七总分分数复查人:合分人:、填空题（每空 1 分，共20 分）分数评卷人1 .智能控制系统的基本类型有 、和_和2 .智能控制具有2个不同于常规控制的本质特点：3 一个理想的智能控制系统应具备的性能、 、 、 、等。4. 人工神经网络常见的输出变换函数有： 和。5. 人工神经网络的学习规则有： 、和。6. 在人工智能领域里知识表示可以分为 和两类。、简答题：（每题5分，共30分）分数评卷人1. 智能控制系统应具有的特点是什么？2. 智能控制系统的结构一般有哪几部分组成，它们之间存在什么关系?3比较智能控制与传统控制的特点。精彩文档4 神经元计算与人工

2、智能传统计算有什么不同?5.人工神经元网络的拓扑结构主要有哪几种？6 简述专家系统与传统程序的区别。三、作图题：(每图4分，共20分)1.画出以下应用场合下适当的隶属函数：(a)我们绝对相信附近的e(t)是“正小”，只有当4“正小”的信心;(b )我们相信附近的e(t)是“正大”，而对于远离分数评卷人e(t)足够远离-时，我们才失去-的e(t)我们很快失去信心;e(t)是(c)随着e(t)从7向左移动，我们很快失去信心，而随着e(t)从-向右移动，我们较慢失去信心。2.画出以下两种情况的隶属函数：(a) 精确集合A x 8 x2的隶属函数；(b) 写出单一模糊(singleton fuzzif

3、ication)隶属函数的数学表达形式，并画出隶属函数 图。四、计算题：(每题10分，共 20分)1. 一个模糊系统的输入和输出的隶属函数如图分数评卷人1所示。试计算以下 条件和规则的隶属函数:(a) 规贝U 1 : If error is zero and chang-in-error is zeroThen force is zero 。 均使用 最小化操作表示蕴含 (us ing minimum opertor) ;(b) 规贝U 2 : If error is zero and chang-in-error is possmallThen force is negsmall均使用乘积操

4、作表示蕴含(using product opertor)2.设论域u5山2,比，血山5，且10.5Au1u2u3u4u5B0.10.710.3qu3u4u5试求A B, A B,AC （补集），BC （补集）五、试论述对BP网络算法的改进。（共10分）分数评卷人题号-一一二三四五六七总分分数附件1智能控制课程试题B分数评卷人复查人:和合分人:一、填空题（每空 1 分，共20分）1 .智能控制的研究对象具备的特点有：2 .智能控制系统的主要类型有： 、 和 。3 确定隶属函数的方法大致有 、 和4. 国内外学者提出了许多面向对象的神经网络控制结构和方法，从大类上看，较具代表性

5、精彩文档分数评卷人分数评卷人的有以下几种： 、 和5. 在一个神经网络中，常常根据处理单元的不同处理功能，将处理单元分成有以下三种：、和。6. 专家系统具有三个重要的特征是： 、和。二、简答题：(每题5分，共30分)1 智能控制有哪些应用领域？试举例说明其工作原理。2 试说明智能控制的三元结构，并画出展示它们之间关系的示意图。3 模糊逻辑与随机事件的联系与区别。4 给出典型的神经元模型。5 BP基本算法的优缺点。6 专家系统的基本组成。三、作图题：(每图4分，共20分)1. 画出以下应用场合下适当的隶属函数：(a) 随着e(t)从向左移动，我们很快失去信心，而随着e(t)从 向右移动，我们较慢

6、失33去信心。(b )我们相信附近的e(t)是正大”，而对于远离 -的e(t)我们很快失去信心；2 2(c) 我们绝对相信附近的e(t)是“正小”，只有当e(t)足够远离时，我们才失去e(t)33是“正小”的信心;2. 画出以下两种情况的隶属函数：(a) 精确集合A x 5 x2的隶属函数；(b) 写出单一模糊(sin gleton fuzzificati on)隶属函数的数学表达形式，并画出隶属函数 图。四、计算题：(每题10分，共 20分)1. 一个模糊系统的输入和输出的隶属函数如图分数评卷人1所示。试计算以下 条件和规则的隶属函数:Then force is possmall(a) 规贝

7、U 1 : If error is zero and chang-in-error is negsmall均使用最小化操作表示蕴含(us ing minimum opertor) ;Then force is negsmall(b) 规贝U 2 : If error is zero and chang-in-error is possmall均使用乘积操作表示蕴含(using product opertor)2.设论域 U Ul,U2, U3 ,U4,U5，且A10.5UiU2U3_ U4U5B0.10.710.3UiU3U4U5试求AB,AB,AC（补集），BC （补集）五

8、、试论述建立专家系统的步骤。（共10分）分数评卷人附件1智能控制课程试题C题号-一一二三四五六七总分分数复查人:合分人:、填空题（每空 1 分，共20 分）分数评卷人1 智能控制是一门新兴的 学科，它具有非常广泛的应用领域，例如 和2 传统控制包括 和3 一个理想的智能控制系统应具备的性能是：、等。4 学习系统的四个基本组成部分是 、分数评卷人5. 专家系统的基本组成部分是二、简答题：（每题5分，共30分）7 .智能控制系统的结构一般有哪几部分组成，它们之间存在什么关系?8 .智能控制系统有哪些类型，各自的特点是什么?9 .比较智能控制与传统控制的特点。4 根据外部环境所提供的知识信息与学习模

9、块之间的相互作用方式，机器学习可以划分为哪几种方式?5 建造专家控制系统大体需要哪五个步骤?分数评卷人6 .为了把专家系统技术应用于直接专家控制系统，在专家系统设计上必须遵循的原则是什 么？三、作图题：(每图4分，共20分)1. 画出以下应用场合下适当的隶属函数：(a)我们绝对相信?附近的e(t)是“正小”，只有当e(t)足够远离-时，我们才失去e(t)是“正小”的信心;(b )我们相信附近的e(t)是正大”，而对于远离的e(t)我们很快失去信心；(c) 随着e(t)从向左移动，我们很快失去信心，而随着e(t)从向右移动，我们较慢失44去信心。2. 画出以下两种情况的隶属函数：(a) 精确集合

10、A x 8 x2的隶属函数；(b) 写出单一模糊(sin gleton fuzzificati on)隶属函数的数学表达形式，并画出隶属函数 图。四、计算题：(每题10分，共 20分)1. 一个模糊系统的输入和输出的隶属函数如图分数评卷人1所示。试计算以下 条件和规则的隶属函数:(a) 规贝U 1 : If error is zero and chang-in-error is zeroThen force is zero均使用最小化操作表示蕴含 (us ing minimum opertor) ;force is n egsmall(b) 规贝U 2 : If error is zero a

11、nd chang-in-error is possmall Then均使用乘积操作表示蕴含(usi ng product opertor) ;2.设论域U mu,比，血，比，且A 20.40.910.5u1u2U3U4U50.10.710.3B 比U3U4U5试求AB,CA B, A（补集），BC （补集）题号-一一二三四五六七总分分数五、画出静态多层前向人工神经网络（BP网络）的结构图，并简述分数评卷人BP神经网络的工作过程（10分）附件1智能控制课程试题D合分人:复查人：、填空题（每空 1 分，共20 分）分数评卷人1 智能控制是一门新兴的 学科，它具有非常广泛的应用领域，例如 2. 智能

12、控制系统的主要类型有:和。3 一个理想的智能控制系统应具备的性智能能是 4 .在设计知识表达方法时，必须从表达方法的 、这四个方面全面加以均衡考虑。5.在一个神经网络中，常常根据处理单元的不同处理功能，将处理单元分成输入单元、输 出单元和三类。、简答题：(每题5分，共30分)分数评卷人10 .智能控制系统的结构一般有哪几部分组成，它们之间存在什么关系?11 .试说明智能控制的三元结构，并画出展示它们之间关系的示意图。12 .比较智能控制与传统控制的特点。4 神经网络应具的四个基本属性是什么？5神经网络的学习方法有哪些？分数评卷人6. 按照专家系统所求解问题的性质，可分为哪几种类型?三、作图题：

13、(每图4分，共20分)1.画出以下应用场合下适当的隶属函数：(a)我们绝对相信附近的e(t)是正小”，只有当e(t)足够远离时，我们才失去e(t)是“正小”的信心；(b )我们相信附近的e(t)是正大”，而对于远离的e(t)我们很快失去信心；33(c) 随着e(t)从向左移动，我们很快失去信心，而随着e(t)从向右移动，我们较慢失44去信心。2. 画出以下两种情况的隶属函数：(a) 精确集合A x 4 x2的隶属函数；(b) 写出单一模糊(singleton fuzzification )隶属函数的数学表达形式，并画出隶属函数图。四、计算题：(每题10分，共 20分)1. 一个模糊系统的输入和

14、输出的隶属函数如图分数评卷人1所示。试计算以下 条件和规则的隶属函数:(a) 规贝U 1 : If error is zero and chang-in-error is zeroThen force is zero 。 均使用 最小化操作表示蕴含 (us ing minimum opertor) ;(b) 规贝U 2 : If error is zero and chang-in-error is possmallThen force is negsmall均使用乘积操作表示蕴含(using product opertor)2.设论域 U Ui,U2,比，血,比，且A10.

15、5UiU2U3U4U5B0.10.710.3UiU3U4U5试求AB,CA B, AC（补集），BC （补集）五、试述专家控制系统的工作原理（共10分）分数评卷人Fuzzy con trol of a ball-bala ncing systemI .IntroductionThe ball-balancing system consists of a cart with an arc made of two parallel pipes on which a steel ball rolls. The cart moves on a pair of tracks horiz on tally

16、 moun ted on a heavy support (Fig. 1). The con trol objective is to bala nee the ball on the top of the arc and at the same time place the cart in a desired position. It is educati on al, because the laboratory rig is sufficie ntly slow for visual in spect ion of differe nt con trol strategies and t

17、he mathematical model is sufficie ntly complex to be challenging. It is a classical pendulum problem, like the ones used as a ben chmark problem for fuzzy and n eural net con trollers, as sales material for fuzzy desig n tools.Initially, the cart is in the middle of the track and the ball is on the

18、left side of the curved arc. A con troller pulls the cart left to get the ball up n ear the middle, the n the con troller adjusts the cart positi on very carefully, without loos ing the ball.Fuzzy controlprovides a format methodology for representing,manipulatingand implementing a human s heuristic

19、knowledge about how to control a system1-3. Here, the fuzzy con trol desig n method will be used to con trol theball-bala ncing system.Fig. 1 Ball-bala ncing laboratory rign . Design objectivea) . Learning the operating principle of the ball-balancing system;b) . Mastering the fuzzy control principl

20、e and design procedure;c) . Enhancing the program ming power using matlab.川.Design requirementsa) . Bala ncing the ball on the top of the arc and at the same time place the cart in a desired positi on.b) . Compari ng the con trol result of the lin ear con troller with that of the fuzzy con troller a

21、nd thinking about the adva ntage of fuzzy con trol to conven ti onal con trol.IV. Desig n prin ciple Model description of the ball-balancing systemIntroducethe state vector x of state variables (y represents cart positionand0.22 rad) represents ball angular deviation)XiXX4The non li near state-space

22、 equati ons 5 are give n as follows:22m(R r)( (r R)mr(sinx3cos x3)x4 mgrsinX3COSX3)2I (R r) z . 2rM (cos x3)m(R r)(M m)(rm (sin x3)(R r) 37r(M m)m(R r)(x：sinx3x：rm(sin3x3)(R r)r(M m)( rm(sin2x3)(R r) rM(迹映血侃 r) r(M m)r(M(r R)(mr2 I)匚2Fm)(HK rm(sin2X3)(R r) rM (cos x3)m(R r)r 八(M m)X4I(R r)r2 2 R rrm

23、 X4(cosx3Sinx3) mgr sinx3)M m2rm(sin x3)(R r)2rM (cos x3)m(R r)(M m)述财咒F2厂I(R r) . . 2rM (cos x3)m(R r)rm (sin2x3)(R r) 3 )(M m)M 3.1 kg is the cart weight,Where R 0.5 m represents cart radius of the arc,F represe nts cart drivi ng force,r-i0.0275 mis the ball radius, r 0.025 m is theball rolli ngra

24、dius,m 0.675 kgis the ball weight,3I 0.024 10 istheballmome nt of in ertia andg 9.81ms2represe nts gravity.The model canbe linearisedaround the origi n.The approximationstothetrig ono metric fun ctio ns are in troduced as follows2 2cos ; 1, sin ;, cos ; 1,sin; 0and the lin ear state-space model can

25、be obta ined as follows& Ax Buy Cx0100000a0b1000ABC00010001000c0d222 .m rgbmr Iwith aMatrices A, B, C are simply and given as follows2，v2Ml mI mr MMl mI mr M2mr g(M m)(R r)(MI mI mr2M )2mr(R r)(MI mI mr2M)The actual values of the con sta nts are(a,b,c,d)( 1.34,0.301,14.3,0.386). Fuzzy controller des

26、ignThere are specific components charactersticof a fuzzy controllerto support adesig n procedure .In the block diagram in Fig. 2, the fuzzy con troller has four main comp onen ts. The followi ng expla ins the block diagram.Fuzzy con trollerFig. 2 Fuzzy con troller architecturea. FuzzificationThe fir

27、st comp onent is fuzzificati on, which con verts each piece of in put data todegrees of membership by a lookup in one of several membership fun cti ons. Thefuzzificati on block thus matches the in put data with the con diti ons of the rules todetermine how well the conditionof each rule matches that

28、 particular inputin sta nee.b. Rule baseThe rule base contains a fuzzy logic quantificationof the expert s linguisticdescripti on of how to achieve good con trol.c. Inference engineFor each rule, the inference engine looks up the membership values in the con diti on of the rule.Aggregati onThe aggre

29、gatio n operati on is used whe n calculat ing the degree offulfillme nt or firing stre ngth of the con diti on of a rule. Aggregati on is equivale nt to fuzzificati on, whe n there is only one in put to the con troller. Aggreagti on is sometimes also called fufilment of the rule or firing strength.A

30、ctivati onThe activati on of a rule is the deduct ion of the con clusi on, possiblyreduced by its firing strength. A rule can be weighted by a priori by a weighting factor, which is its degree of con fide nee.The degree of con fide nce is determ ined by the desig ner, or a lear ning program trying t

31、o adapt the rules to some in put-output relatio nship.Accumulati on All activated con clusi ons are accumulated using the max operatio n.d. Defuzzificati onThe result ing fuzzy set must be con verted to a nu mber that can be sent to the processes as a control signal. This operation is called defuzzi

32、fication.The output sets can be sin glet ons, but they can also be lin ear comb in ati ons of the in puts, or even a function of the in puts. The T-S fuzzy model was proposed byTakagi and Suge no in an effort to develop a systematic approach to gen erati ngfuzzy rules from a give n in put-output dat

33、a set 4. Its rule structure has thefollowi ng form:Ri: ifx1isA1, x2isA2,L,xmisAm,then yP0iP)iRXF2x2LWhereAj is a fuzzy set , Xj is the j th in put,m is the number of in puts, y isthe output specified by the ruleR , Rji is the truth value parameter. Using fuzzyinference based upon product-sum-gravity

34、 at a give n in put ,x Xi,X2丄,XmT , thefinal output of the fuzzy model ,average of yyn(i 1,2,L ,n)is inferred by taking the weightedmi iyi 1niwhere n is the number of fuzzy rules, the weight, i implies the overall truthvalue of the i th rule calculated based on the degrees of membership values:m.Aj(

35、Xj) Computer simulationThe simulati on results can be obta ined by the desig ned program using matlab.Initial conditionscan be changed and controllergains can be adjusted. Then thedesired results can be obta in ed.V . Design procedure精彩文档a) . The model of the ball-bala ncing system has bee n give n;

36、b) . Fuzzy con troller desig n;Fuzzy con trol desig n esse ntially amounts to (1) choos ing the fuzzy con trollerin puts andoutputs (2) choos ing the preprocess ing that is n eeded for the con troller in putsand possibly postprocess ing that is n eeded for the outputs, and (3) desig ningeach of the

37、four comp onents of the fuzzy con troller show n in Fig. 2.c) . Computer simulatio n.Refere nces1 . K. M. Passino and S. Yurkovich(1997). Fuzzy control, 1st edn, Addision WesleyLon gma n, Colifor nia.2 .Cai Zixing.In tellige ntCon trol:Prin ciples,Tech niq uesand Applicati ons.Si ngapore-New Jersey:

38、 World Scie ntific Publishers, Dec. 1997.3 . Pedrycz, W.(1993). Fuzzy con trol and fuzzy systems, sec ond edn, Wiley and Sons, New York.4 . Takagi, T. and Sugno, M. (1985). Fuzzy identificationof systems and itsapplications to modeling and control, IEEE Trans. Systems, Man & Cybernetics 15(1): 116-1

39、32.Speed control design for a vehicle system using fuzzy logicIntroductionEngine and other automobile systems are in creas in gly con trolled electr on ically.This has led to improved fuel economy, reduced pollution, improved driving safety and reduced manu facturi ng costs. However the automobile i

40、s a hostile environment: especially in the engine compartme nt, where high temperature, humidity, vibrati on, electrical interferenee and a fine cocktail of potentially corrosive pollutants are prese nt. These hostile factors may cause electrical con tacts to deteriorate, surface resistances to fall

41、 and sensitive electronic systems to fail in a variety of modes. Some of these failure modes will be benign, whereas others may be dan gerous and cause accide nts and endan ger to huma n life.A cruise control system, or vehicle speed control system can keep a vehicles speed constant on long runs and

42、 therefore may help prevent driver fatigue 2-5. If the driver hands over speed control to a cruise control system, then the capability of the system to control speed to the set value is just as critical to safety as is the capability of the driver to control speed manually. So the cruise control sys

43、temdesign is imperative and important to an automobile.n . Design requirementsa) . Desig ning con troller using fuzzy logic;b) . Making the automobile s speed keep constant.IH . Model description of the automobileThe dyn amics of the automobile 1 are give n as follows1 2&(t) -( Ap(t) d f(t)m辄)-(f(t)

44、 u(t)Where u is the controlin put( u 0 represe nts a throttlein putand u 0represe nts a brake in put),m 1300kg is the mass of the vehicle,Ap 0.3 Ns2/m2is its aerodynamicdrag, d 100N is a constantfrictionalforce,f is thedrivi ng/brak ing force, and0.2sec is saturated at1000N ).We can use fuzzy contro

45、l method to design a cruise control system. Obviously, the fuzzy cruise con trol desig n objective is to develop a fuzzy con troller that regulates a vehicle s speed (t) to a driver-specified value d (t).IV. Speed con trol desig n using fuzzy logicFuzzy con trol logic and n eural n etworks are other

46、 examples of methodologies con trol engin eers are exam ining to address the con trol of very complex systems. A good fuzzy con trol logicapplicati on is in cruise con trol area.1) Desig n of PI fuzzy con trollerSupposethat we wishto be able to tracka step or ramp changein thedriver-specified speed

47、valued (t) very accurately. API fuzzy con trollercan beused as shownin Fig. 1. In Fig. 1, the fuzzy controlleris denotedbyg。，gp nd g2 are scali ng gains; and b(t) is the in put of the in tegrator.d(t)g1$u(t)Fuzzy con troller)gog2?b(t)Automobile(t)Fig. 1 Speed con trol system using a PI fuzzy con tro

48、llerFind the differential equation that describes the closed-loop system. Let the state be x x1, x2 ,x3T , f, bT and find a system of three first-order ordinary differentialequations that can be used by the Runge-Kutta method in thesimulati on of the closed-loop system. differe ntial equati ons.is u

49、sed to represe nt the con troller in theFor the reference in put, three differe nt test sig nals can be used as follows:a: Test in put 1 makes d(t)=18m/sec (40.3 mph) for 0 t 10 and d(t) = 22 m/sec (49.2 mph) for 10 t 30.b: Test in put 2 makes d (t) =18m/sec (40.3 mph) for 0 t 10 and d (t) in crease

50、s lin early (a ramp) from 18 to 22m/sec by t 25sec, and the nd (t) 22for 25 t 30.c: Test in put 3 makes d (t) =22 for 0 t and we use x(0) as the in itial con diti on (this represe nts start ing the vehicle at rest and sudde nly comma nding a large in crease speed).Use x(0)18,197.2,20T for test in pu

51、t 1 and 2.Design the fuzzy controllerto get less than 2% overshoot, a rise-timebetween 5 and 7 sec, and a settling time of less than 8 sec (i.e., reach to within 2% of the final value within 8 sec) for the jump from 18 to 22m/sec in “test input 1”that is defi ned above. Also, for the ramp in put (“ test in