Abstract
The identification of nonlinear systems using neural networks has become a widely studied research area in recent years. System identification mainly consists of two steps: the first is to choose an appropriate identification model and the second is to adjust the parameters of the model according to some adaptive laws so that the response of the model to an input signal can approximate the response of the real system to the same input. Since neural networks have good approximation capabilities and inherent adaptivity features, they provide a powerful tool for identification of systems with unknown nonlinearities (Antsaklis, 1990; Miller et al. 1990).
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© 2001 Springer-Verlag London
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Liu, G.P. (2001). Sequential Nonlinear Identification. In: Nonlinear Identification and Control. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-0345-5_2
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DOI: https://doi.org/10.1007/978-1-4471-0345-5_2
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1076-7
Online ISBN: 978-1-4471-0345-5
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