Title | Fuzzy Systems [electronic resource] : Modeling and Control / edited by Hung T. Nguyen, Michio Sugeno |
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Imprint | Boston, MA : Springer US : Imprint: Springer, 1998 |

Connect to | http://dx.doi.org/10.1007/978-1-4615-5505-6 |

Descript | XXI, 519 p. online resource |

SUMMARY

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, organized, and up-to-date presentation of fuzzy systems engineering methods. The book includes detailed material and extensive bibliographies, written by leading experts in the field, on topics such as: Use of fuzzy logic in various control systems. Fuzzy rule-based modeling and its universal approximation properties. Learning and tuning techniques for fuzzy models, using neural networks and genetic algorithms. Fuzzy control methods, including issues such as stability analysis and design techniques, as well as the relationship with traditional linear control. Fuzzy sets relation to the study of chaotic systems, and the fuzzy extension of set-valued approaches to systems modeling through the use of differential inclusions. Fuzzy Systems: Modeling and Control is part of The Handbooks of Fuzzy Sets Series. The series provides a complete picture of contemporary fuzzy set theory and its applications. This volume is a key reference for systems engineers and scientists seeking a guide to the vast amount of literature in fuzzy logic modeling and control

CONTENT

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 -- 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? and When is, Say, Neural Modeling Better? -- References -- Fuzzy and Linear Controllers -- 5.1 Introduction -- 5.2 Modal Equivalence Principle -- 5.3 Application to PI Controllers -- 5.4 Application to State Feedback Fuzzy Controllers -- 5.5 Equivalence for Sugenoโ{128}{153}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 -- 9.5 Fuzzy Neurocomputing โ{128}{148} 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 -- 13.1 Introduction -- 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 -- 13.8 Nondegenerate Periodicities and Chaos -- 13.9 Examples of Fuzzy Chaos -- 13.10 Conclusion -- 13.11 Appendix -- References

Mathematics
Operations research
Decision making
Artificial intelligence
Mathematical logic
Discrete mathematics
Mechanical engineering
Electrical engineering
Mathematics
Mathematical Logic and Foundations
Discrete Mathematics
Electrical Engineering
Operation Research/Decision Theory
Mechanical Engineering
Artificial Intelligence (incl. Robotics)