Neural networks for nonlinear dynamic system modelling and identification
作者:
S. CHEN,
S. A. BILLINGS,
期刊:
International Journal of Control
(Taylor Available online 1992)
卷期:
Volume 56,
issue 2
页码: 319-346
ISSN:0020-7179
年代: 1992
DOI:10.1080/00207179208934317
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
Many real-world systems exhibit complex nonlinear characteristics and cannot be treated satisfactorily using linear systems theory. A neural network which has the ability to learn sophisticated nonlinear relationships provides an ideal means of modelling complicated nonlinear systems. This paper addresses the issues related to the identification of nonlinear discrete-time dynamic systems using neural networks. Three network architectures, namely the multi-layer perceptron, the radial basis function network and the functional-link network, are presented and several learning or identification algorithms are derived. Advantages and disadvantages of these structures are discussed and illustrated using simulated and real data. Particular attention is given to the connections between existing techniques for nonlinear systems identification and some aspects of neural network methodology, and this demonstrates that certain techniques employed in the neural network context have long been developed by the control engineering community.
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