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Chemical Optimization Algorithm for Fuzzy Controller Design.

By: Astudillo, Leslie.
Contributor(s): Melin, Patricia | Castillo, Oscar.
Material type: TextTextSeries: eBooks on Demand.SpringerBriefs in Applied Sciences and Technology: Publisher: Dordrecht : Springer, 2014Description: 1 online resource (81 p.).ISBN: 9783319052458.Subject(s): Automatic control | Fuzzy systems | Nonlinear control theory | Soft computingGenre/Form: Electronic books.Additional physical formats: Print version:: Chemical Optimization Algorithm for Fuzzy Controller DesignDDC classification: 629.8 Online resources: Click here to view this ebook.
Contents:
Preface; Contents; 1 Introduction; Abstract; References; 2 Theory and Background; Abstract; 2.1 Nature-Inspired Metaheuristics; 2.2 Artificial Chemistry; 2.2.1 The Set of Molecules S; 2.2.2 The Set of Rules R; 2.2.3 Reactor Algorithm A-Dynamics; 2.3 Fuzzy Logic; 2.3.1 Fuzzy Sets; 2.3.2 Fuzzy Logic Controller; 2.4 Related Work; References; 3 Chemical Definitions; Abstract; 3.1 Chemical Definitions; Reference; 4 The Proposed Chemical Reaction Algorithm; Abstract; 4.1 ElementsCompounds; 4.2 Chemical Reactions; 4.3 Synthesis Reactions; 4.4 Decomposition Reactions
4.5 Single-Substitution Reactions4.6 Double-Substitution Reactions; References; 5 Application Problems; Abstract; 5.1 Complex Benchmark Functions; 5.2 Control of an Autonomous Mobile Robot Using Fuzzy Logic; 5.2.1 Definition of the Mobile Robot; 5.2.2 Tracking Controller of Mobile Robot; 5.2.3 Control of the Kinematic model; 5.2.4 The Fuzzy Logic Tracking Controller; 5.3 Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic; References; 6 Simulation Results; Abstract; 6.1 Results of the CRA Applied to the Complex Benchmark Functions
6.2 Results of the CRA Applied to the Fuzzy Control of an Autonomous Mobile Robot6.2.1 Finding k1, k2, k3; 6.2.2 Optimization of a Fuzzy Logic Controller; 6.2.2.1 Case 1; 6.2.2.2 Case 2; 6.2.2.3 Case 3; 6.2.3 Best Result Applying GAs; 6.3 Optimization of a Type-2 Fuzzy Logic Controller; References; 7 Conclusions; Abstract; Reference; Appendix A; Appendix B; Index
Summary: In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions.This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic m
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Preface; Contents; 1 Introduction; Abstract; References; 2 Theory and Background; Abstract; 2.1 Nature-Inspired Metaheuristics; 2.2 Artificial Chemistry; 2.2.1 The Set of Molecules S; 2.2.2 The Set of Rules R; 2.2.3 Reactor Algorithm A-Dynamics; 2.3 Fuzzy Logic; 2.3.1 Fuzzy Sets; 2.3.2 Fuzzy Logic Controller; 2.4 Related Work; References; 3 Chemical Definitions; Abstract; 3.1 Chemical Definitions; Reference; 4 The Proposed Chemical Reaction Algorithm; Abstract; 4.1 ElementsCompounds; 4.2 Chemical Reactions; 4.3 Synthesis Reactions; 4.4 Decomposition Reactions

4.5 Single-Substitution Reactions4.6 Double-Substitution Reactions; References; 5 Application Problems; Abstract; 5.1 Complex Benchmark Functions; 5.2 Control of an Autonomous Mobile Robot Using Fuzzy Logic; 5.2.1 Definition of the Mobile Robot; 5.2.2 Tracking Controller of Mobile Robot; 5.2.3 Control of the Kinematic model; 5.2.4 The Fuzzy Logic Tracking Controller; 5.3 Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic; References; 6 Simulation Results; Abstract; 6.1 Results of the CRA Applied to the Complex Benchmark Functions

6.2 Results of the CRA Applied to the Fuzzy Control of an Autonomous Mobile Robot6.2.1 Finding k1, k2, k3; 6.2.2 Optimization of a Fuzzy Logic Controller; 6.2.2.1 Case 1; 6.2.2.2 Case 2; 6.2.2.3 Case 3; 6.2.3 Best Result Applying GAs; 6.3 Optimization of a Type-2 Fuzzy Logic Controller; References; 7 Conclusions; Abstract; Reference; Appendix A; Appendix B; Index

In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions.This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic m

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