Soft Computing for Control of Non-Linear Dynamical Systems
The book describes the application of soft computing techniques to modelling, simulation and control of non-linear dynamical systems. Hybrid intelligence systems, which integrate different techniques and mathematical models, are also presented. The book covers the basics of fuzzy logic, neural netwo...
| Main Authors: | , |
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| Corporate Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Heidelberg :
Physica-Verlag HD : Imprint: Physica,
2001.
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| Edition: | 1st ed. 2001. |
| Series: | Studies in Fuzziness and Soft Computing,
63 |
| Subjects: | |
| Online Access: | Full Text via HEAL-Link |
Table of Contents:
- 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
- 2.5 Fuzzy Modelling
- 2.6 Summary
- 3 Neural Networks for Control
- 3.1 Backpropagation for Feedforward Networks
- 3.2 Adaptive Neuro-Fuzzy Inference Systems
- 3.3 Neuro-Fuzzy Control
- 3.4 Adaptive Model-Based 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
- 4.4 Summary
- 5 Dynamical Systems Theory
- 5.1 Basic Concepts of Dynamical Systems
- 5.2 Controlling Chaos
- 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
- 7.4 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
- 8.7 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
- 9.4 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
- 13.3 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.
