智能控制——模糊逻辑和神经网络方面

World Scientific Series in Robotics and Intelligent Systems Pub Date : 1993-03-01 DOI:10.1142/1721

C. Harris, C. G. Moore, Martin Brown

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引用次数: 252

摘要

指数:1。智能控制概论1.1基础1.2智能控制要求与体系结构1.3智能控制方法1.4基于知识的系统1.5模糊逻辑1.6控制中的模糊逻辑1.7神经控制器1.8高级智能控制器1.9参考文献模糊逻辑简介2.1模糊集与逻辑2.2模糊推理与构成2.3去模糊化3模糊控制器的结构与设计3.1简介3.2模糊集理论的应用3.3模糊控制器结构问题3.4模糊控制器的设计要求静态模糊控制器4.1简介4.2口头或专家询问式控制器设计4.3模糊PID控制器4.4参数确定模糊PID控制器4.5语言规则反演模糊逻辑控制器4.6基于聚类的模糊逻辑控制器5。自组织模糊逻辑控制5.1绪论5.2基于控制规则的SOFLIC 5.3基于规则的SOFLIC应用5.4基于控制规则的SOFLIC系统设计6。间接自组织模糊控制器6.1简介6.2自组织模糊模型与预测器6.3关系因果反转6.4控制器设计6.5自适应模糊控制器6.6间接自适应模糊控制器仿真示例6.7嵌套模糊控制器和混合模糊控制器7间接模糊自适应控制的实例研究7.1船舶航向调节7.2城市公交的轨道控制7.3自动道路车辆控制与引导7.4间接模糊自适应控制的观察8控制与建模中的神经网络逼近能力8.1绪论8.2人工神经网络的逼近能力8.3神经控制中的多层感知器8.4建模与控制中的径向基函数b样条神经网络与模糊逻辑9.1绪论9.2多项式基函数9.3 b样条指导函数9.4多元基函数9.5加权自适应9.6 b样条神经网络非线性时间序列预测因子与建模9.7模糊逻辑和单层联想记忆神经网络的比较9.8结论附录:数学前提A.1度量空间A.2赋范度量空间A.3代数A.4赋范空间中的逼近内容

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Intelligent Control - Aspects of Fuzzy Logic and Neural Nets

Index: 1. An Introduction to Intelligent Control 1.1 Preliminaries 1.2 Intelligent Control Requirements and Architectures 1.3 Approaches to Intelligent Control 1.4 Knowledge Based Systems 1.5 Fuzzy Logic 1.6 Fuzzy Logic in Control 1.7 Neurocontrollers 1.8 Higher Level Intelligent Controllers 1.9 Bibliographical Notes 2. Introductory Fuzzy Logic 2.1 Fuzzy Sets and Logic 2.2 Fuzzy Inference and Composition 2.3 Defuzzification 3. Fuzzy Logic Controller Structure and Design 3.1 Introduction 3.2 Applications of Fuzzy Set Theory 3.3 Fuzzy Logic Controller Structural Issues 3.4 Design Requirements of Fuzzy Logic Controllers 4. The Static Fuzzy Logic Controller 4.1 Introduction 4.2 Controller Design by Verbalisation or Expert Interrogation 4.3 The Fuzzy PID Controller 4.4 Parametrically Determined Fuzzy PID Controllers 4.5 Linguistic Rule Inversion Fuzzy Logic Controllers 4.6 Cluster Based Fuzzy Logic Controllers 5. Self-Organising Fuzzy Logic Control 5.1 Introduction 5.2 Control Rule Base SOFLICs 5.3 Rule Based SOFLIC Applications 5.4 Systematic Design of Control Rule Based SOFLIC 6. Indirect Self-Organising Fuzzy Logic Controllers 6.1 Introduction 6.2 Self-Organising Fuzzy Models and Predictors 6.3 Relation Causality Inversion 6.4 Controller Design 6.5 Adaptive Fuzzy Controller 6.6 A Simulation Example of Indirect Adaptive Fuzzy Logic Control 6.7 Nested and Hybrid Fuzzy Controllers 7. Case Studies of Indirect Adaptive Fuzzy Control 7.1 Regulation of a Ship's Heading 7.2 Track Control of a City Bus 7.3 Autonomous Road Vehicle Control and Guidance 7.4 Observations on Indirect Fuzzy Adaptive Control 8. Neural Network Approximation Capability for Control and Modelling 8.1 Introduction 8.2 Approximation Capability of Artificial Neural Networks 8.3 Multilayer Perceptrons in Neurocontrol 8.4 Radial Basis Functions in Modelling and Control 9. The B-spline Neural Network and Fuzzy Logic 9.1 Introduction 9.2 Polynomial Basis Functions 9.3 B-splines for Guidance 9.4 Multivariate Basis Functions 9.5 Weighted Adaptation 9.6 B-spline Neural Net Nonlinear Time Series Predictors and Modelling 9.7 A Comparison between Fuzzy Logic and Single Layer Associative Memory Neural Networks 9.8 Conclusions Appendix: Mathematical Prerequisites A.1 Metric Spaces A.2 Normed Metric Spaces A.3 Algebras A.4 Approximation in Normed Spaces Contents

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World Scientific Series in Robotics and Intelligent Systems

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