Abstract
Chapters 2 and 3 have shown that neural networks can be employed to identify dynamic systems. The main advantages of neural networks over conventional identification methods include simplicity of implementation and good approximation properties [Warwick et aI, 1992]. In feedforward network based identification schemes, neural networks are used to represent the implied static mapping between the available input and output data. The network structures (number of layers and number of units in each layer) are predefined and remain unchanged both during and after training. Successful identification is often dependent on proper pre-estimation of the network structure.
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© 1995 Springer-Verlag London Limited
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Pham, D.T., Liu, X. (1995). Modelling and Prediction Using GMDH Networks. In: Neural Networks for Identification, Prediction and Control. Springer, London. https://doi.org/10.1007/978-1-4471-3244-8_4
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DOI: https://doi.org/10.1007/978-1-4471-3244-8_4
Publisher Name: Springer, London
Print ISBN: 978-1-4471-3246-2
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