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.