Fuzzy Systems

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? andWhen 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