Nonlinear Identification and Control: A Neural Network Approach

ISBN-13: 9781852333423 / Angielski / Twarda / 2001 / 210 str.

G.P. Liu

Opis

The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies . . ., new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series otTers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. The time for nonlinear control to enter routine application seems to be approaching. Nonlinear control has had a long gestation period but much ofthe past has been concerned with methods that involve formal nonlinear functional model representations. It seems more likely that the breakthough will come through the use of other more flexible and amenable nonlinear system modelling tools. This Advances in Industrial Control monograph by Guoping Liu gives an excellent introduction to the type of new nonlinear system modelling methods currently being developed and used. Neural networks appear prominent in these new modelling directions. The monograph presents a systematic development of this exciting subject. It opens with a useful tutorial introductory chapter on the various tools to be used. In subsequent chapters Doctor Liu leads the reader through identification, and then onto nonlinear control using nonlinear system neural network representations.

Spis treści

1. Neural Networks.- 1.1 Introduction.- 1.2 Model of a Neuron.- 1.3 Architectures of Neural Networks.- 1.3.1 Single Layer Networks.- 1.3.2 Multilayer Networks.- 1.3.3 Recurrent Networks.- 1.3.4 Lattice Networks.- 1.4 Various Neural Networks.- 1.4.1 Radial Basis Function Networks.- 1.4.2 Gaussian RBF Networks.- 1.4.3 Polynomial Basis Function Networks.- 1.4.4 Fuzzy Neural Networks.- 1.4.5 Wavelet Neural Networks.- 1.4.6 General Form of Neural Networks.- 1.5 Learning and Approximation.- 1.5.1 Background to Function Approximation.- 1.5.2 Universal Approximation.- 1.5.3 Capacity of Neural Networks.- 1.5.4 Generalisation of Neural Networks.- 1.5.5 Error Back Propagation Algorithm.- 1.5.6 Recursive Learning Algorithms.- 1.5.7 Least Mean Square Algorithm.- 1.6 Applications of Neural Networks.- 1.6.1 Classification.- 1.6.2 Filtering.- 1.6.3 Modelling and Prediction.- 1.6.4 Control.- 1.6.5 Hardware Implementation.- 1.7 Mathematical Preliminaries.- 1.8 Summary.- 2. Sequential Nonlinear Identification.- 2.1 Introduction.- 2.2 Variable Neural Networks.- 2.2.1 Variable Grids.- 2.2.2 Variable Networks.- 2.2.3 Selection of Basis Functions.- 2.3 Dynamical System Modelling by Neural Networks.- 2.4 Stable Nonlinear Identification.- 2.5 Sequential Nonlinear Identification.- 2.6 Sequential Identification of Multivariable Systems.- 2.7 An Example.- 2.8 Summary.- 3. Recursive Nonlinear Identification.- 3.1 Introduction.- 3.2 Nonlinear Modelling by VPBF Networks.- 3.3 Structure Selection of Neural Networks.- 3.3.1 Off-line Structure Selection.- 3.3.2 On-line Structure Selection.- 3.4 Recursive Learning of Neural Networks.- 3.5 Examples.- 3.6 Summary.- 4. Multiobjective Nonlinear Identification.- 4.1 Introduction.- 4.2 Multiobjective Modelling with Neural Networks.- 4.3 Model Selection by Genetic Algorithms.- 4.3.1 Genetic Algorithms.- 4.3.2 Model Selection.- 4.4 Multiobjective Identification Algorithm.- 4.5 Examples.- 4.6 Summary.- 5. Wavelet Based Nonlinear Identification.- 5.1 Introduction.- 5.2 Wavelet Networks.- 5.2.1 One-dimensional Wavelets.- 5.2.2 Multi-dimensional Wavelets.- 5.2.3 Wavelet Networks.- 5.3 Identification Using Fixed Wavelet Networks.- 5.4 Identification Using Variable Wavelet Networks.- 5.4.1 Variable Wavelet Networks.- 5.4.2 Parameter Estimation.- 5.5 Identification Using B-spline Wavelets.- 5.5.1 One-dimensional B-spline Wavelets.- 5.5.2 n-dimensional B-spline Wavelets.- 5.6 An Example.- 5.7 Summary.- 6. Nonlinear Adaptive Neural Control.- 6.1 Introduction.- 6.2 Adaptive Control.- 6.3 Adaptive Neural Control.- 6.4 Adaptation Algorithm with Variable Networks.- 6.5 Examples.- 6.6 Summary.- 7. Nonlinear Predictive Neural Control.- 7.1 Introduction.- 7.2 Predictive Control.- 7.3 Nonlinear Neural Predictors.- 7.4 Predictive Neural Control.- 7.5 On-line Learning of Neural Predictors.- 7.6 Sequential Predictive Neural Control.- 7.7 An Example.- 7.8 Summary.- 8. Variable Structure Neural Control.- 8.1 Introduction.- 8.2 Variable Structure Control.- 8.3 Variable Structure Neural Control.- 8.4 Generalised Variable Structure Neural Control.- 8.5 Recursive Learning for Variable Structure Control.- 8.6 An Example.- 8.7 Summary.- 9. Neural Control Application to Combustion Processes.- 9.1 Introduction.- 9.2 Model of Combustion Dynamics.- 9.3 Neural Network Based Mode Observer.- 9.4 Output Predictor and Controller.- 9.5 Active Control of a Simulated Combustor.- 9.6 Active Control of an Experimental Combustor.- 9.7 Summary.