Neural networks for identification, prediction and control
著者
書誌事項
Neural networks for identification, prediction and control
Springer-Verlag, c1995
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注記
Includes bibliographical references and indexes
内容説明・目次
内容説明
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 programmes written in C for neural-network-based system identification and prediction to facilitate practical experimentation with neural network techniques are included.
目次
- Artificial neural networks
- dynamic system identification using feedforward neural networks
- dynamic system modelling
- modelling and prediction using GMDH networks
- financial prediction using GMDH networks
- financial prediction using neural networks
- neural network controllers
- neuromorphic fuzzy controller design
- robot manipulator control using neural networks.
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