Computational Intelligence in Control Engineering
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Table of contents:Introduction Expert Systems in Industry Intelligent Control Techniques of Intelligent Control Elements of Fuzzy Logic Fuzzy Reasoning The Fuzzy Control Algorithm Fuzzy Industrial Controllers Real-time Fuzzy Control Model-Based Fuzzy Control Neural Control Neural Network Training Rule-Based Neural Control Neuro-Fuzzy Control Evolutionary Computation Simulated Annealing Evolutionary Design of Controllers Bibliography Appendix A: Case Study: Design of a Fuzzy Controller Using MATLABPresenting the theoretical and practical developments of an autonomous decision-making methodology...
Table of contents:
Introduction Expert Systems in Industry Intelligent Control Techniques of Intelligent Control Elements of Fuzzy Logic Fuzzy Reasoning The Fuzzy Control Algorithm Fuzzy Industrial Controllers Real-time Fuzzy Control Model-Based Fuzzy Control Neural Control Neural Network Training Rule-Based Neural Control Neuro-Fuzzy Control Evolutionary Computation Simulated Annealing Evolutionary Design of Controllers Bibliography Appendix A: Case Study: Design of a Fuzzy Controller Using MATLAB
Presenting the theoretical and practical developments of an autonomous decision-making methodology, this work describes the strides made by intelligent systems and soft computing for the control of industrial systems. It uses practical examples of qualitative control techniques tested in industry and provides suitable intelligent computational algorithms and interfaces for industrial applications.
Introduction Expert Systems in Industry Intelligent Control Techniques of Intelligent Control Elements of Fuzzy Logic Fuzzy Reasoning The Fuzzy Control Algorithm Fuzzy Industrial Controllers Real-time Fuzzy Control Model-Based Fuzzy Control Neural Control Neural Network Training Rule-Based Neural Control Neuro-Fuzzy Control Evolutionary Computation Simulated Annealing Evolutionary Design of Controllers Bibliography Appendix A: Case Study: Design of a Fuzzy Controller Using MATLAB
Presenting the theoretical and practical developments of an autonomous decision-making methodology, this work describes the strides made by intelligent systems and soft computing for the control of industrial systems. It uses practical examples of qualitative control techniques tested in industry and provides suitable intelligent computational algorithms and interfaces for industrial applications.