Fuzzy number

About: Fuzzy number is a research topic. Over the lifetime, 35606 publications have been published within this topic receiving 972544 citations.

Intelligent Control: Aspects of Fuzzy Logic and Neural Nets

Abstract: Index: 1 An Introduction to Intelligent Control 11 Preliminaries 12 Intelligent Control Requirements and Architectures 13 Approaches to Intelligent Control 14 Knowledge Based Systems 15 Fuzzy Logic 16 Fuzzy Logic in Control 17 Neurocontrollers 18 Higher Level Intelligent Controllers 19 Bibliographical Notes 2 Introductory Fuzzy Logic 21 Fuzzy Sets and Logic 22 Fuzzy Inference and Composition 23 Defuzzification 3 Fuzzy Logic Controller Structure and Design 31 Introduction 32 Applications of Fuzzy Set Theory 33 Fuzzy Logic Controller Structural Issues 34 Design Requirements of Fuzzy Logic Controllers 4 The Static Fuzzy Logic Controller 41 Introduction 42 Controller Design by Verbalisation or Expert Interrogation 43 The Fuzzy PID Controller 44 Parametrically Determined Fuzzy PID Controllers 45 Linguistic Rule Inversion Fuzzy Logic Controllers 46 Cluster Based Fuzzy Logic Controllers 5 Self-Organising Fuzzy Logic Control 51 Introduction 52 Control Rule Base SOFLICs 53 Rule Based SOFLIC Applications 54 Systematic Design of Control Rule Based SOFLIC 6 Indirect Self-Organising Fuzzy Logic Controllers 61 Introduction 62 Self-Organising Fuzzy Models and Predictors 63 Relation Causality Inversion 64 Controller Design 65 Adaptive Fuzzy Controller 66 A Simulation Example of Indirect Adaptive Fuzzy Logic Control 67 Nested and Hybrid Fuzzy Controllers 7 Case Studies of Indirect Adaptive Fuzzy Control 71 Regulation of a Ship's Heading 72 Track Control of a City Bus 73 Autonomous Road Vehicle Control and Guidance 74 Observations on Indirect Fuzzy Adaptive Control 8 Neural Network Approximation Capability for Control and Modelling 81 Introduction 82 Approximation Capability of Artificial Neural Networks 83 Multilayer Perceptrons in Neurocontrol 84 Radial Basis Functions in Modelling and Control 9 The B-spline Neural Network and Fuzzy Logic 91 Introduction 92 Polynomial Basis Functions 93 B-splines for Guidance 94 Multivariate Basis Functions 95 Weighted Adaptation 96 B-spline Neural Net Nonlinear Time Series Predictors and Modelling 97 A Comparison between Fuzzy Logic and Single Layer Associative Memory Neural Networks 98 Conclusions Appendix: Mathematical Prerequisites A1 Metric Spaces A2 Normed Metric Spaces A3 Algebras A4 Approximation in Normed Spaces Contents

Uncertainty reasoning based on cloud models in controllers

Abstract: The methodology of fuzzy reasoning has been shown to be very useful technology for modeling complex nonlinear systems However, the most commonly used method for reasoning with fuzzy systems models, the Mamdani-Zadeh paradigm, faces many criticisms, particularly from the probability community A new mathematical representation of linguistic concepts is presented in this paper With the new model of normal compatibility clouds and a virtual rule engine, a novel uncertainty reasoning technology is proposed It not only serves as a foundation of linguistic control, but also integrating fuzziness and randomness in an inseparable way A case study is given to clean up many doubts raised in the debate between fuzzy theory and probability theory researchers, and to give a good interpretation of the Mamdani-Zadeh operations for the defuzzification strategy as well The architecture of such a controller shows the advantages in hardware implementations