Soft Computing for Control of Non-Linear Dynamical Systems (Hardcover, 2001)

1 Introduction to Control of Non-Linear Dynamical Systems.- 2 Fuzzy Logic.- 2.1 Fuzzy Set Theory.- 2.2 Fuzzy Reasoning.- 2.3 Fuzzy Inference Systems.- 2.4 Type-2 Fuzzy Logic Systems.- 2.4.1 Type-2 Fuzzy Sets.- 2.4.2 Type-2 Fuzzy Systems.- 2.5 Fuzzy Modelling.- 2.6 Summary.- 3 Neural Networks for Control.- 3.1 Backpropagation for Feedforward Networks.- 3.1.1 The Backpropagation Learning Algorithm.- 3.1.2 Backpropagation Multilayer Perceptions.- 3.2 Adaptive Neuro-Fuzzy Inference Systems.- 3.2.1 ANFIS Architecture.- 3.2.2 Learning Algorithm.- 3.3 Neuro-Fuzzy Control.- 3.3.1 Inverse Learning.- 3.3.2 Specialized Learning.- 3.4 Adaptive Model-Based Neuro-Control.- 3.4.1 Indirect Neuro-Control.- 3.4.2 Direct Neuro-Control.- 3.4.3 Parameterized Neuro-Control.- 3.5 Summary.- 4 Genetic Algorithms and Simulated Annealing.- 4.1 Genetic Algorithms.- 4.2 Simulated Annealing.- 4.3 Applications of Genetic Algorithms.- 4.3.1 Evolving Neural Networks.- 4.3.1.1 Evolving Weights in a Fixed Network.- 4.3.1.2 Evolving Network Architectures.- 4.3.2 Evolving Fuzzy Systems.- 4.4 Summary.- 5 Dynamical Systems Theory.- 5.1 Basic Concepts of Dynamical Systems.- 5.2 Controlling Chaos.- 5.2.1 Controlling Chaos through Feedback.- 5.2.1.1 Ott-Grebogi-Yorke Method.- 5.2.1.2 Pyragas’s Control Methods.- 5.2.1.3 Controlling Chaos by Chaos.- 5.2.2 Controlling Chaos without Feedback.- 5.2.2.1 Control through Operating Conditions.- 5.2.2.2 Control by System Design.- 5.2.2.3 Taming Chaos.- 5.2.3 Method Selection.- 5.3 Summary.- 6 Hybrid Intelligent Systems for Time Series Prediction.- 6.1 Problem of Time Series Prediction.- 6.2 Fractal Dimesion of an Object.- 6.3 Fuzzy Logic for Object Classification.- 6.4 Fuzzy Estimation of the Fractal Dimension.- 6.5 Fuzzy Fractal Approach for Time Series Analysis and Prediction.- 6.6 Neural Network Approach for Time Series Prediction.- 6.7 Fuzzy Fractal Approach for Pattern Recognition.- 6.8 Summary.- 7 Modelling Complex Dynamical Systems with a Fuzzy Inference System for Differential Equations.- 7.1 The Problem of Modelling Complex Dynamical Systems.- 7.2 Modelling Complex Dynamical Systems with the New Fuzzy Inference System.- 7.3 Modelling Robotic Dynamic Systems with the New Fuzzy Interence System.- 7.3.1 Mathematical Modelling of Robotic Systems.- 7.3.2 Fuzzy Modelling of Robotic Dynamic Systems.- 7.3.3 Experimental Results.- 7.4 Modelling Aircraft Dynamic Systems with the New Fuzzy Inference System.- 7.5 Summary.- 8 A New Theory of Fuzzy Chaos for Simulation of Non-Linear Dynamical Systems.- 8.1 Problem Description.- 8.2 Towards a New Theory of Fuzzy Chaos.- 8.3 Fuzzy Chaos for Behavior Identification in the Simulation of Dynamical Systems.- 8.4 Simulation of Dynamical Systems.- 8.5 Method for Automated Parameter Selection Using Genetic Algorithms.- 8.6 Method for Dynamic Behavior Identification Using Fuzzy Logic.- 8.6.1 Behavior Identification Based on the Analytical Properties of the Model.- 8.6.2 Behavior Identification Based on the Fractal Dimension and the Lyapunov Exponents.- 8.7 Simulation Results for Robotic Systems.- 8.8 Summary.- 9 Intelligent Control of Robotic Dynamic Systems.- 9.1 Problem Description.- 9.2 Mathematical Modelling of Robotic Dynamic Systems.- 9.3 Method for Adaptive Model-Based Control.- 9.3.1 Fuzzy Logic for Dynamic System Modelling.- 9.3.2 Neuro-Fuzzy-Fractal Adaptive Model-Based Control.- 9.4 Adaptive Control of Robotic Dynamic Systems.- 9.5 Simulation Results for Robotic Dynamic Systems.- 9.6 Summary.- 10 Controlling Biochemical Reactors.- 10.1 Introduction.- 10.2 Fuzzy Logic for Modelling.- 10.3 Neural Networks for Control.- 10.4 Adaptive Control of a Non-Linear Plant.- 10.5 Fractal Identification of Bacteria.- 10.6 Experimantal Results.- 10.7 Summary.- 11 Controlling Aircraft Dynamic Systems.- 11.1 Introduction.- 11.2 Fuzzy Modelling of Dynamical Systems.- 11.3 Neural Networks for Control.- 11.4 Adaptive Control of Aircraft Systems.- 11.5 Experimental Results.- 11.6 Summary.- 12 Controlling Electrochemical Processes.- 12.1 Introduction.- 12.2 Problem Description.- 12.3 Fuzzy Method for cControl.- 12.4 Neuro-Fuzzy Methof for Control.- 12.5 Neuro-Fuzzy-Genetic Method for Control.- 12.6 Experimental Results for the Three Hybrid Approaches.- 12.7 Summary.- 13 Controlling International Trade Dynamics.- 13.1 Introduction.- 13.2 Mathematical Modelling of International Trade.- 13.2.1 Oscillations in Autonomous Economies.- 13.2.2 International Trade as a Perturbation of Internal Oscillations.- 13.3 Fuzzy Logic for Model Selection.- 13.4 Adaptive Model-Based Control of International Trade.- 13.5 Simulation Results for Control of International Trade.- 13.6 Summary.- References.