智能控制试卷及答案4套.doc

第 26 页 共 26 页附件1智能控制 课程试题A题号一二三四五六七总分分数合分人： 复查人： 一、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 。2智能控制具有2个不同于常规控制的本质特点： 和 。3一个理想的智能控制系统应具备的性能是 、 、 、 、 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。二、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。4神经元计算与人工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）分数评卷人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，我们较慢失去信心。2. 画出以下两种情况的隶属函数：（a）精确集合 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）分数评卷人1. 一个模糊系统的输入和输出的隶属函数如图1所示。试计算以下条件和规则的隶属函数：（a）规则1：If error is zero and chang-in-error is zero Then force is zero。 均使用最小化操作表示蕴含(using minimum opertor)；（b）规则2：If error is zero and chang-in-error is possmall Then force is negsmall。 均使用乘积操作表示蕴含(using product opertor)；2. 设论域，且试求（补集），（补集）五、试论述对BP网络算法的改进。（共 10 分）分数评卷人附件1智能控制 课程试题 B题号一二三四五六七总分分数合分人： 复查人： 一、填空题（每空 1 分，共 20分）分数评卷人1智能控制的研究对象具备的特点有： 、 和 。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）分数评卷人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）分数评卷人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，我们较慢失去信心。（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；（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)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 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）分数评卷人1. 一个模糊系统的输入和输出的隶属函数如图1所示。试计算以下条件和规则的隶属函数：（a）规则1：If error is zero and chang-in-error is negsmall Then force is possmall。 均使用最小化操作表示蕴含(using minimum opertor)；（b）规则2：If error is zero and chang-in-error is possmall Then force is negsmall。 均使用乘积操作表示蕴含(using product opertor)；2. 设论域，且试求（补集），（补集）五、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题号一二三四五六七总分分数合分人： 复查人： 一、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 、 、 、 。5专家系统的基本组成部分是 、 、 。二、简答题：（每题 5 分，共 30 分）分数评卷人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？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）分数评卷人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，我们较慢失去信心。2. 画出以下两种情况的隶属函数：（a）精确集合 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）分数评卷人1. 一个模糊系统的输入和输出的隶属函数如图1所示。试计算以下条件和规则的隶属函数：（a）规则1：If error is zero and chang-in-error is zero Then force is zero。 均使用最小化操作表示蕴含(using minimum opertor)；（b）规则2：If error is zero and chang-in-error is possmall Then force is negsmall。 均使用乘积操作表示蕴含(using product opertor)；2. 设论域，且试求（补集），（补集）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BP神经网络的工作过程（ 10 分）分数评卷人。附件1智能控制 课程试题D题号一二三四五六七总分分数合分人： 复查人： 一、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智能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 、 、 等。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。二、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？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：（每图 4 分，共 20 分）分数评卷人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，我们较慢失去信心。2. 画出以下两种情况的隶属函数：（a）精确集合 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）分数评卷人1. 一个模糊系统的输入和输出的隶属函数如图1所示。试计算以下条件和规则的隶属函数：（a）规则1：If error is zero and chang-in-error is zero Then force is zero。 均使用最小化操作表示蕴含(using minimum opertor)；（b）规则2：If error is zero and chang-in-error is possmall Then force is negsmall。 均使用乘积操作表示蕴含(using product opertor)；2. 设论域，且试求（补集），（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 control of a ball-balancing system. 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 horizontally mounted on a heavy support (Fig. 1). The control objective is to balance the ball on the top of the arc and at the same time place the cart in a desired position. It is educational, because the laboratory rig is sufficiently slow for visual inspection of different control strategies and the mathematical model is sufficiently complex to be challenging. It is a classical pendulum problem, like the ones used as a benchmark problem for fuzzy and neural net controllers, as sales material for fuzzy design tools.Initially, the cart is in the middle of the track and the ball is on the left side of the curved arc. A controller pulls the cart left to get the ball up near the middle, then the controller adjusts the cart position very carefully, without loosing the ball.Fuzzy control provides a format methodology for representing, manipulating and implementing a humans heuristic knowledge about how to control a system 1-3. Here, the fuzzy control design method will be used to control the ball-balancing system.Fig. 1 Ball-balancing laboratory rig. Design objectivea). Learning the operating principle of the ball-balancing system; b). Mastering the fuzzy control principle and design procedure; c). Enhancing the programming power using matlab. Design requirementsa). Balancing the ball on the top of the arc and at the same time place the cart in a desired position. b). Comparing the control result of the linear controller with that of the fuzzy controller and thinking about the advantage of fuzzy control to conventional control. Design principle Model description of the ball-balancing systemIntroduce the state vector of state variables (represents cart position and represents ball angular deviation) The nonlinear state-space equations 5 are given as follows: Where represents cart radius of the arc, is the cart weight, represents cart driving force, is the ball radius, is the ball rolling radius, is the ball weight, is the ball moment of inertia andrepresents gravity. The model can be linearised around the origin. The approximations to the trigonometric functions are introduced as followsand the linear state-space model can be obtained as followsMatrices are simply and given as follows with , The actual values of the constants are . Fuzzy controller design There are specific components characterstic of a fuzzy controller to support a design procedure. In the block diagram in Fig. 2, the fuzzy controller has four main components. The following explains the block diagram. Fig. 2 Fuzzy controller architecturea. FuzzificationThe first component is fuzzification, which converts each piece of input data to degrees of membership by a lookup in one of several membership functions. The fuzzification block thus matches the input data with the conditions of the rules to determine how well the condition of each rule matches that particular input instance.b. Rule base The rule base contains a fuzzy logic quantification of the experts linguistic description of how to achieve good control. c. Inference engine For each rule, the inference engine looks up the membership values in the condition of the rule. Aggregation The aggregation operation is used when calculating the degree of fulfillment or firing strength of the condition of a rule. Aggregation is equivalent to fuzzification, when there is only one input to the controller. Aggreagtion is sometimes also called fufilment of the rule or firing strength.Activation The activation of a rule is the deduction of the conclusion, possibly reduced by its firing strength. A rule can be weighted by a priori by a weighting factor, which is its degree of confidence. The degree of confidence is determined by the designer, or a learning program trying to adapt the rules to some input-output relationship.Accumulation All activated conclusions are accumulated using the max operation. d. Defuzzification The resulting fuzzy set must be converted to a number that can be sent to the processes as a control signal. This operation is called defuzzification. The output sets can be singletons, but they can also be linear combinations of the inputs, or even a function of the inputs. The T-S fuzzy model was proposed by Takagi and Sugeno in an effort to develop a systematic approach to generating fuzzy rules from a given input-output data set 4. Its rule structure has the following form:Where is a fuzzy set, is the input, is the number of inputs， is the output specified by the rule ， is the truth value parameter. Using fuzzy inference based upon product-sum-gravity at a given input , ，the final output of the fuzzy model ,is inferred by taking the weighted average of where is the number of fuzzy rules, the weight, implies the overall truth value of the rule calculated based on the degrees of membership values: Computer simulationThe simulation results can be obtained by the designed program using matlab. Initial conditions can be changed and controller gains can be adjusted. Then the desired results can be obtained. Design procedurea). The model of the ball-balancing system has been given;b). Fuzzy controller design;Fuzzy control design essentially amounts to (1) choosing the fuzzy controller inputs and outputs (2) choosing the preprocessing that is needed for the controller inputs and possibly postprocessing that is needed for the outputs, and (3) designing each of the four components of the fuzzy controller shown in Fig. 2.c). Computer simulation. References1. K. M. Passino and S. Yurkovich(1997). Fuzzy control, 1st edn, Addision Wesley Longman, Colifornia. 2. Cai Zixing. Intelligent Control: Principles, Techniques and Applications. Singapore-New Jersey: World Scientific Publishers, Dec. 1997.3. Pedrycz, W.(1993). Fuzzy control and fuzzy systems, second edn, Wiley and Sons, New York.4. Takagi, T. and Sugno, M. (1985). Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Systems, Man & Cybernetics 15(1): 116-132. Speed control design for a vehicle system using fuzzy logic.IntroductionEngine and other automobile systems are increasingly controlled electronically. This has led to improved fuel economy, reduced pollution,improved driving safety and reduced manufacturing costs. However the automobileis a hostile environment: especially in the engine compartment, where high temperature, humidity, vibration, electrical interference and a fine cocktail of potentially corrosive pollutants are present. These hostile factors may cause electrical contacts to deteriorate, surface resistances to fall and sensitive electronic systems to fail in a variety of modes. Some of these failure modes will be benign, whereas others may be dangerous and cause accidents and endanger to human life.A cruise control system, or vehicle speed control system can keep a vehicles speed constant on long runs and 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 system design is imperative and important to an automobile. Design requirements a). Designing controller using fuzzy logic; b). Making the automobiles speed keep constant. . Model description of the automobileThe dynamics of the automobile 1 are given as followsWhere is the control input ( represents a throttle input and represents a brake input), is the mass of the vehicle, is its aerodynamic drag, is a constant frictional force, is the driving/braking force, and sec is saturated at ).We can use fuzzy control method to design a cruise control system. Obviously, the fuzzy cruise control design objective is to develop a fuzzy controller that regulates a vehicles speed to a driver-specified value. Speed control design using fuzzy logicFuzzy control logic and neural networks are other examples of methodologies control engineers are examining to address the control of very complex systems. A good fuzzy control logic application is in cruise control area.1) Design of PI fuzzy controllerSuppose that we wish to be able to track a step or ramp change in the driver-specified speed value very accurately. A “PI fuzzy controller” can be used as shown in Fig. 1. In Fig. 1, the fuzzy controller is denoted by ; andare scaling gains; and is the input of the integrator. Fig. 1 Speed control system using a PI fuzzy controller Find the differential equation that describes the closed-loop system. Let the state be and find a system of three first-order ordinary differential equations that can be used by the Runge-Kutta method in the simulation of the closed-loop system. is used to represent the controller in the differential equations. For the reference input, three different test signals can be used as follows:a: Test input 1 makes =18m/sec (40.3 mph) for and 22 m/sec (49.2 mph) for .b: Test input 2 makes =18m/sec (40.3 mph) for and increases linearly (a ramp) from 18 to 22 by , and then for .c: Test input 3 makes =22 for and we use as the initial condition (this represents starting the vehicle at rest and suddenly commanding a large increase speed).Use for test input 1 and 2. Design the fuzzy controller to get less than 2% overshoot, a rise-time between 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 22 in “test input 1” that is defined above. Also, for the ramp input (“test input2” above) it must have less than 1 mph (0.447 ) steady-state error (i.e., at the end of the ramp part of the input have less than 1 mph error). Fully specify the controller (e.g., the membership functions, rule-base defuzzification, etc.) and simulate the closed-loop system to demonstrate that it performs properly. Provide plots of and on the same axis and on a different plot. For test input 3 find the rise-time, overshoot, 2% settling time, and steady-state error for the closed-loop system for the controller that you designed to meet the specifications for test input 1 and 2. Using the Runge-Kutta method and integration step size of 0.01, the simulation results can be shown as follows. Test input 1Fig. 2 Vehicle speeds and the output of fuzzy controller using test input 1Test input 2Fig. 3 Vehicle speeds and the output of fuzzy controller using test input 2Test input 3Fig. 4 Vehicle speeds and the output of fuzzy controller using test input 32) Design of PD fuzzy controllerSuppose that you are concerned with tracking a step change in accurately and that you use the PD fuzzy controller shown in Fig. 5. To represent the derivative, simply use a backward differenceWhere is the integration step size in your simulation (or it could be your sampling period in an implementation). Fig. 5 Speed control system using a PD fuzzy controller Design a PD fuzzy controller to get less than 2% overshoot, a rise-t