Titlebook: Neural Networks for Identification, Prediction and Control; Duc Truong Pham,Xing Liu Book 1995 Springer-Verlag London Limited 1995 backpro

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Book 1995Neural 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 prior

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Neuromorphic Fuzzy Controller Design,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.

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Modelling and Prediction Using GMDH Networks,res (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.

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Artificial Neural Networks,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.

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Robot Manipulator Control Using Neural Networks,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.

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Dynamic System Identification Using Recurrent Neural Networks,lements 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.

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