Cover Image
Read Now

Fuzzy Systems Modeling and Control

The analysis and control of complex systems have been the main motivation for the emergence of fuzzy set theory since its inception. It is also a major research field where many applications, especially industrial ones, have made fuzzy logic famous. This unique handbook is devoted to an extensive, o...

Full description

Bibliographic Details
Other Authors: Hung T. Nguyen (Editor), Sugeno, Michio (Editor)
Format: eBook
Language:English
Published: New York, NY Springer US 1998, 1998
Edition:1st ed. 1998
Series:The Handbooks of Fuzzy Sets
Subjects:
Operations Research
Mathematical Logic
Electrical And Electronic Engineering
Artificial Intelligence
Electrical Engineering
Discrete Mathematics
Mechanical Engineering
Mathematical Logic And Foundations
Operations Research And Decision Theory
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • Introduction: The Real Contribution of Fuzzy Systems
  • References
  • Methodology of Fuzzy Control
  • 1.1 Introduction: Why Fuzzy Control
  • 1.2 How to Translate Fuzzy Rules into the Actual Control: General Idea
  • 1.3 Membership Functions and Where They Come From
  • 1.4 Fuzzy Logical Operations
  • 1.5 Modeling Fuzzy Rule Bases
  • 1.6 Inference From Several Fuzzy Rules
  • 1.7 Defuzzification
  • 1.8 The Basic Steps of Fuzzy Control: Summary
  • 1.9 Tuning
  • 1.10 Methodologies of Fuzzy Control: Which Is The Best?
  • References
  • to Fuzzy Modeling
  • 2.1 Introduction
  • 2.2 Takagi-Sugeno Fuzzy Model
  • 2.3 Sugeno-Kang Method
  • 2.4 Sofia
  • 2.5 Conclusion
  • References
  • Fuzzy Rule-Based Models and Approximate Reasoning
  • 3.1 Introduction
  • 3.2 Linguistic Models
  • 3.3 Inference with Fuzzy Models
  • 3.4 Mamdani (Constructive) and Logical (Destructive) Models
  • 3.5 Linguistic Models With Crisp Outputs
  • 3.6 Multiple Variable Linguistic Models
  • 9.5 Fuzzy Neurocomputing — a Fusion of Fuzzy and Neural Technology
  • 9.6 Constructing Hybrid Neurofuzzy Systems
  • 9.7 Summary
  • References
  • Neural Networks and Fuzzy Logic
  • 10.1 Introduction
  • 10.2 Liquid Level Control Problem
  • 10.3 Fuzzy Rule Development
  • 10.4 Integrated System Architectures
  • 10.5 FNN3 Training Algorithm
  • 10.6 Conclusions
  • References
  • Fuzzy Genetic Algorithms
  • 11.1 Introduction
  • 11.2 What is a Genetic Algorithm?
  • 11.3 Fuzzy Genetic Algorithms
  • 11.4 Fuzzy Genetic Programming
  • References
  • Fuzzy Systems, Viability Theory and Toll Sets
  • 12.1 Introduction
  • 12.2 Convexification Procedures
  • 12.3 Toll Sets
  • 12.4 Fuzzy or Toll Differential Inclusions
  • References
  • Chaos and Fuzzy Systems
  • 13.1Introduction
  • 13.2 Preliminaries
  • 13.3 Dynamical Systems and Chaos
  • 13.4 Information Content of Fuzzy Sets
  • 13.5 Chaotic Mappings on (Dn, d?)
  • 13.6 r-Fuzzification.
  • 13.7 Chaos and Fuzzification
  • 5.2 Modal Equivalence Principle
  • 5.3 Application to PI Controllers
  • 5.4 Application to State Feedback Fuzzy Controllers
  • 5.5 Equivalence for Sugeno’s Controllers
  • 5.6 Conclusion
  • References
  • Designs of Fuzzy Controllers
  • 6.1 Introduction
  • 6.2 Fuzzy Control Techniques
  • 6.3 The FC as a Nonlinear Transfer Element
  • 6.4 Heuristic Control and Model Based Control
  • 6.5 Supervisory Control
  • 6.6 Adaptive Control
  • References
  • Stability of Fuzzy Controllers
  • 7.1 Introduction
  • 7.2 Stability Conditions Based on Lyapunov Approach
  • 7.3 Fuzzy Controller Design
  • References
  • Learning and Tuning of Fuzzy Rules
  • 8.1 Introduction
  • 8.2 Learning Fuzzy Rules
  • 8.3 Tuning Fuzzy Rules
  • 8.4 Learning and Tuning Fuzzy Rules
  • 8.5 Summary and Conclusion
  • References
  • Neurofuzzy Systems
  • 9.1 Introduction
  • 9.2 Synergy of Neural Networks and Fuzzy Logic
  • 9.3 Fuzzy sets in the technology of neurocomputing
  • 9.4 Hybrid Fuzzy Neural Computing Structures
  • 3.7 Takagi-Sugeno-Kang (TSK) Models
  • 3.8 A General View of Fuzzy Systems Modeling
  • 3.9 MICA Operators
  • 3.10 Aggregation in Fuzzy Systems Modeling
  • 3.11 Dynamic Fuzzy Systems Models
  • 3.12 TSK Models of Dynamic Systems
  • 3.13 Conclusion
  • References
  • Fuzzy Rule Based Modeling as a Universal Approximation Tool
  • 4.1 Introduction
  • 4.2 Main Universal Approximation Results
  • 4.3 Can We Guarantee That the Approximation Function has the Desired Properties (Such as Smoothness, Simplicity, Stability of the Resulting Control, etc.)?
  • 4.4 Auxiliary Approximation Results
  • 4.5 How to Make the Approximation Results More Realistic
  • 4.6 From All Fuzzy Rule Based Modeling Methodologies That are Universal Appriximation Tools, Which Methodology Should We Choose?
  • 4.7 A Natural Next Question: When Should We Choose Fuzzy Rule Based Modeling in the First Place? andWhen is, Say, Neural Modeling Better?
  • References
  • Fuzzy and Linear Controllers
  • 5.1 Introduction
  • 13.8 Nondegenerate Periodicities and Chaos
  • 13.9 Examples of Fuzzy Chaos
  • 13.10 Conclusion
  • 13.11 Appendix
  • References

Similar Items