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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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Artificial Neural Networks

Abstract
Artificial neural networks are computational models of the brain. There are many types of neural networks representing the brain’s structure and operation with varying degrees of sophistication. This chapter provides an introduction to the main types of networks and presents examples of each type.
Duc Truong Pham, Xing Liu

Chapter 2. Dynamic System Identification Using Feedforward Neural Networks

Abstract
A dynamic system can be described by two types of models: input-output models and state-space models. This chapter describes the use of feedforward neural networks to learn to act as both types of models.
Duc Truong Pham, Xing Liu

Chapter 3. Dynamic System Identification Using Recurrent Neural Networks

Abstract
As mentioned in Chapter 1, neural networks can be classified as feedforward networks and recurrent networks. In feedforward networks, the processing elements are connected in such a way that all signals flow in one direction from input units to output units. In recurrent networks there are both feedforward and feedback connections along which signals can propagate in opposite directions.
Duc Truong Pham, Xing Liu

Chapter 4. Modelling and Prediction Using GMDH Networks

Abstract
Chapters 2 and 3 have shown that neural networks can be employed to identify dynamic systems. The main advantages of neural networks over conventional identification methods include simplicity of implementation and good approximation properties [Warwick et aI, 1992]. In feedforward network based identification schemes, neural networks are used to represent the implied static mapping between the available input and output data. The network structures (number of layers and number of units in each layer) are predefined and remain unchanged both during and after training. Successful identification is often dependent on proper pre-estimation of the network structure.
Duc Truong Pham, Xing Liu

Chapter 5. Financial Prediction Using Neural Networks

Abstract
This chapter discusses the application of neural networks to stock market and exchange rate prediction. The first section illustrates the use of the multilayer perception network for stock market prediction. The remainder of the chapter describes experiments with the multilayer perception network, the GMDH network, and the Elman network in exchange rate prediction.
Duc Truong Pham, Xing Liu

Chapter 6. Neural Network Controllers

Abstract
Neural networks are developed by morphologically and computationally simulating a human brain. Although, as seen in previous chapters, the precise operation details of artificial neural networks are quite different from human brains, they are similar in three aspects. First, a neural network consists of a very large number of simple processing elements (the neurons). Second, each neuron is connected to a large number of other neurons. Third, the functionality of the networks is determined by modifying the strengths of connections during a learning phase [Psaltis et al, 1988; Hunt et al, 1992; Warwick et ai, 1992]. Efforts have been made to find efficient approaches for control from physiological studies of the brain. Research over the last twenty years has revealed the architecture and performance characteristics of the brain as a controller [Albus, 1975; Ito, 1984; Kawato et al, 1987] and has shown that neural network controllers have important advantages over conventional controllers. The first advantage is that a neural network controller can efficiently utilise a much larger amount of sensory information in planning and executing a control action than an industrial controller can. The second advantage is that a neural network controller has the collective processing capability that enables it to respond quickly to complex sensory inputs while the execution speed of sophisticated control algorithms in a conventional controller is severely limited.
Duc Truong Pham, Xing Liu

Chapter 7. Neuromorphic Fuzzy Controller Design

Abstract
This chapter shows that a single-input single-output (SISO) Fuzzy Logic Controller (FLC) [Mamdani, 1974; Lee, 1990a, 1990b] can be modelled as a neural network which can be trained using a Genetic Algorithm (GA). The GA is employed to determine the membership functions for the input variable, the quantisation levels of the output variable and the elements of the relation matrix of the FLC. The reasons for such a neuromorphic FLC are provided. The structure of the NN model proposed for an FLC and its GA-based training procedure are explained. Results for the simulated control of a time-delayed linear second-order plant and a non-linear plant are also given. In this chapter, it is assumed that the reader is familiar with fuzzy logic control and genetic algorithms. F or a basic introduction to these topics, see Appendix B and Appendix C.
Duc Truong Pham, Xing Liu

Chapter 8. Robot Manipulator Control Using Neural Networks

Abstract
The control of a multi-input-multi-output (MIMO) plant is a difficult problem when the plant is nonlinear and time-varying and there are dynamic interactions between the plant variables. A good example of such a plant is an articulated robot with two or more joints handling a changeable load.
Duc Truong Pham, Xing Liu

Backmatter

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