Get Neural Networks for Identification, Prediction and Control PDF

By Duc Truong Pham, Xing Liu (auth.)

ISBN-10: 1447132440

ISBN-13: 9781447132448

ISBN-10: 1447132467

ISBN-13: 9781447132462

In contemporary years, there was a growing to be curiosity in making use of neural networks to dynamic platforms id (modelling), prediction and regulate. Neural networks are computing structures characterized by means of the power to benefit from examples instead of having to be programmed in a standard feel. Their use permits the behaviour of complicated structures to be modelled and estimated and exact regulate to be accomplished via education, with no priori information regarding the platforms' constructions or parameters. This booklet describes examples of functions of neural networks In modelling, prediction and keep watch over. the themes coated contain identity of basic linear and non-linear techniques, forecasting of river degrees, inventory marketplace costs and forex charges, and keep watch over of a time-delayed plant and a two-joint robotic. those functions hire the foremost kinds of neural networks and studying algorithms. The neural community varieties thought of intimately are the muhilayer perceptron (MLP), the Elman and Jordan networks and the Group-Method-of-Data-Handling (GMDH) community. furthermore, cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy common sense structures also are offered. the most studying set of rules followed within the purposes is the normal backpropagation (BP) set of rules. Widrow-Hoff studying, dynamic BP and evolutionary studying also are described.

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Get Neural Networks for Identification, Prediction and Control PDF

Lately, there was a growing to be curiosity in using neural networks to dynamic structures id (modelling), prediction and keep an eye on. Neural networks are computing platforms characterized via the power to profit from examples instead of having to be programmed in a traditional experience. Their use allows the behaviour of complicated structures to be modelled and estimated and exact keep an eye on to be completed via education, and not using a priori information regarding the platforms' constructions or parameters.

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A. (1961) Asymptotic Mdhods in the Theory 0/ No"UnelU Oscilllztio"s, New York: Gordon and Breach. Chen, S. A. (1990), Neural networks for nonlinear dynamic system modelling and identification, Internatio"al JOlUlfal 0/ Control, 56(2), 319-346. Cybenko, G. (1989) Approximation by superpositions of a sigmoidal function, MatlIematics o/Control, Signals, IUId Systems, 2, 303-314 . Funahashi, K (1989) On the approximate realization of continuous mappings by neural networks, Neural Networks, 2, 183-192.

1990), Neural networks for nonlinear dynamic system modelling and identification, Internatio"al JOlUlfal 0/ Control, 56(2), 319-346. Cybenko, G. (1989) Approximation by superpositions of a sigmoidal function, MatlIematics o/Control, Signals, IUId Systems, 2, 303-314 . Funahashi, K (1989) On the approximate realization of continuous mappings by neural networks, Neural Networks, 2, 183-192. Harber, R. and Unbehauen, H. (1990) Structure identification of nonlinear dynamic systems - a survey on input I output approaches, AU'olfUltica, 26(4), 651-677.

1960) Adaptive switching circuits, Proc. 1960 IRE WESCON Convention Record, Part 4, IRE, New York. 96-104 Chapter 2 Dynamic System Identification Using Feedforward Neural Networks A dynamic system can be described by two types of models: input-{)utput models and state-space models . This chapter describes the use of feedforward neural networks to learn to act as both types of models. 1 Input-Output Model An input-output model describes a dynamic system based on input and output data. In the discrete-time domain, an input-output model can be of the NARMAX type [Chen and Billings, 1990J or the parametric Hammerstein type [Iserman et ai, 1992].

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Neural Networks for Identification, Prediction and Control by Duc Truong Pham, Xing Liu (auth.)


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