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Neural Networks for Identification, Prediction and Control [Hardcover]

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Item description for Neural Networks for Identification, Prediction and Control by D. T. Pham & Xing Liu...

This publication describes examples of applications of neural networks in modelling, prediction and control. Topics covered include identification of general linear and nonlinear processes, forecasting of river levels, stock market prices, currency exchange rates, and control of a time-delayed plant and a two-joint robot. The neural network types considered are the multilayer perceptron (MLP), the Elman and Jordan networks, the Group-Method-of-Data-Handling (GMDH), the cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy logic systems. The algorithms presented are the standard backpropagation (BP) algorithm, the Widrow-Hoff learning, dynamic BP and evolutionary learning. Full listings of computer programs written in C for neural-network-based system identification and prediction to facilitate practical experimentation with neural network techniques are included.

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Item Specifications...

Pages   238
Est. Packaging Dimensions:   Length: 0.75" Width: 6.5" Height: 9.75"
Weight:   1.1 lbs.
Binding  Hardcover
Release Date   Jul 7, 1995
Publisher   Springer
ISBN  3540199594  
ISBN13  9783540199595  

Availability  0 units.

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Product Categories

1Books > Special Features > Formats
2Books > Subjects > Computers & Internet > Computer Science > Artificial Intelligence > Neural Networks
3Books > Subjects > Professional & Technical > Engineering > Electrical & Electronics > Electricity Principles
4Books > Subjects > Professional & Technical > Engineering > Electrical & Optical > Electronics > General
5Books > Subjects > Professional & Technical > Engineering > Electrical & Optical > General
6Books > Subjects > Professional & Technical > Engineering > General
7Books > Subjects > Professional & Technical > Engineering > Industrial, Manufacturing & Operational Syste > General
8Books > Subjects > Professional & Technical > Professional Science > Mathematics
9Books > Subjects > Science > General
10Books > Subjects > Science > Mathematics > Chaos & Systems

Reviews - What do customers think about Neural Networks for Identification, Prediction and Control?

State-of-the-art neural networks for complex control  Mar 5, 2001
The book is both clear and interesting to read. It is invaluable for people wishing to implement neural network systems for modelling, prediction or control. Many state-of-the-art neural network techniques are explained, including MLP, LVQ, Elman, Jordan, Kohonen and ART. In addition, a large number of examples are given of how neural networks can be employed for controlling complex non-linear systems.
Not Good  Jun 10, 2000
This book was quite disappointing. The majority of examples were linear (which is not the strengh of the use of Neural Networks) especially in the presentation of system identification. The material itself needs to be updated to the current methods. It seems that the authors tried to cover too much material (i.e. Genetic Algs., Fuzzy logic) which resulted in not enough detail especially mathematically.
very clear and useful for studies about neural network  Apr 12, 1999
I am studying civil engineering and I found this book very useful for my studies:I have investigated the application of neural networks in the identification of Tensegrities, that is civil sistems made up of rigid beams and cables. The identifications I made, using Matlab, were essentially statics, but I had to write something dealing with dynamic identification. I found this book very clear, and it helped me much more than other various articles and books I read on the subject.

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