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Neural Networks for Identification, Prediction and Control (Paperback, Softcover reprint of the original 1st ed. 1995)
Loot Price: R1,458
Discovery Miles 14 580
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Neural Networks for Identification, Prediction and Control (Paperback, Softcover reprint of the original 1st ed. 1995)
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Total price: R1,468
Discovery Miles: 14 680
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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.
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