Neural Networks for Identification, Prediction and Control

ISBN-13: 9781447132462 / Angielski / Miękka / 2012 / 238 str.

Duc T. Pham; Xing Liu

Opis

In recent years, there has been a growing interest in applying neural networks to dynamic systems identification (modelling), prediction and control. Neural networks are computing systems characterised by the ability to learn from examples rather than having to be programmed in a conventional sense. Their use enables the behaviour of complex systems to be modelled and predicted and accurate control to be achieved through training, without a priori information about the systems' structures or parameters. This book describes examples of applications of neural networks In modelling, prediction and control. The topics covered include identification of general linear and non-linear processes, forecasting of river levels, stock market prices and currency exchange rates, and control of a time-delayed plant and a two-joint robot. These applications employ the major types of neural networks and learning algorithms. The neural network types considered in detail are the muhilayer perceptron (MLP), the Elman and Jordan networks and the Group-Method-of-Data-Handling (GMDH) network. In addition, cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy logic systems are also presented. The main learning algorithm adopted in the applications is the standard backpropagation (BP) algorithm. Widrow-Hoff learning, dynamic BP and evolutionary learning are also described.

Spis treści

1 Artificial Neural Networks.- l.1 Types of Neural Networks.- 1.1.1 Structural Categorisation.- l.1.2 Learning Algorithm Categorisation.- l.2 Example Neural Networks.- l.2.1 Multi-layer Perception (MLP).- 1.2.2 Learning Vector Quantization (LVQ) Network.- 1.2.3 CMAC Network.- 1.2.4 Group Method of Data Handling (GMDH) Network.- 1.2.5 Hopfield Network.- l.2.6 Elman and Jordan Nets.- 1.2.7 Kohonen Network.- 1.2.8 ART Network.- 1.3 Summary.- References.- 2 Dynamic System Identification Using Feedforward Neural Networks.- 2.1Dynamic System Descriptions.- 2.1.1 Input-output Model.- 2.1.2 State-space Model.- 2.2 Identification Based on System Inputs and Outputs.- 2.3 Identification Based on Measurable System States.- 2.4 Input-output Model Identification.- 2.4.1 Simulation Investigations.- 2.4.2 Results.- 2.5 State-space Model Identification.- 2.5.1 Plant Model and Identification Architecture.- 2.5.2 Simulations.- 2.6 Discussion.- 2.6.1 Hybrid Networks.- 2.6.2 Input-output versus State-space Modelling.- 2.7 Analysis of the Hybrid Network.- 2.8 Summary.- References.- 3 Dynamic System Modelling Using Recurrent Neural Networks.- 3.1 Basic Ehnan Network.- 3.1.1 Structure and Principle of Ehnan Network.- 3.1.2 Analysis of Ehnan Network.- 3.2 Modified Ehnan Network.- 3.3 Dynamic System Modelling.- 3.4 Further Analysis of Ehnan Networks.- 3.4.1 Dynamic Backpropagation in Ehnan Networks.- 3.4.2 Relation with the Modified Ehnan Network.- 3.5 Summary.- References.- 4 Modelling and Prediction Using GMDH Networks.- 4.1 N-Adaline Networks and Widrow-Hoff Learning.- 4.2 GMDH Network Based on N-Adalines.- 4.3 Applications.- 4.4 Discussion.- 4.5 Summary.- References.- 5 Financial Prediction Using Neural Networks.- 5.1 Stock Market Prediction.- 5.1.1 System Overview.- 5.1.2 Prediction Simulation.- 5.2 Currency Exchange Rate Prediction.- 5.2.1 Prediction Based on Neural Networks.- 5.3 Data Sets Adopted for Simulation.- 5.4 Prediction Based on GMDH Networks.- 5.5 Prediction Based on Multilayer Perception Networks.- 5.6 Prediction Based on Recurrent Networks.- 5.7 Discussion.- 5.8 Summary.- References.- 6 Neural Network Controllers.- 6.1 Neural Network Controllers.- 6.1.1 CMAC.- 6.1.2 Hierarchical Neural Network Model.- 6.1.3 Multilayered Neural Network Controller.- 6.2 Comparison of Neural Network Controllers.- 6.2.1 Hierarchical Neural Network Model and Multilayered Neural Network Controller.- 6.2.2 Multilayered Neural Network Controller (or Hierarchical Neural Network Model) and CMAC.- 6.2.3 Common Aspects of Neural Network Controllers.- 6.2.4 Comparison with Adaptive Controllers.- 6.3 Summary.- References.- 7 Neuromorphic Fuzzy Controller Design.- 7.1 Integrating Neural Networks and FLCs.- 7.1.1 Representation of a Simple Fuzzy Logic Controller as a Neural Network.- 7.1.2 Learning Algorithm.- 7.2 Results of Neuromorphic Fuzzy Controllers Design.- 7.2.1 Plant 1.- 7.2.2 Plant2.- 7.3 Summary.- References.- 8 Robot Manipulator Control Using Neural Networks.- 8.1 Modelling of a Multi-joint Robot.- 8.2 Control System.- 8.3 Application to a Two-joint Robot Arm.- 8.4 Discussion.- 8.5 Summary.- References.- Appendix A Introduction to Some Conventional Techniques of Identification, Prediction and Control.- Appendix B Fuzzy Sets and Fuzzy Logic Control.- Appendix C Genetic Algorithms.- Appendix D Program: Feedforward Network for System Identification.- Appendix E Program: Modified Elman Network for Identification.- Appendix F Program: GMDH Network for Prediction.- Author Index.