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An introduction to radial basis functions for system identification: a comparison with other neural network methods

Warwick, K. and Craddock, R. (1997) An introduction to radial basis functions for system identification: a comparison with other neural network methods. In: 35th IEEE Conference on Decision and Control, 11-13 Dec 1996, Kobe, Japan, pp. 464-469.

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Official URL: http://dx.doi.org/10.1109/CDC.1996.574355

Abstract/Summary

A look is taken at the use of radial basis functions (RBFs), for nonlinear system identification. RBFs are firstly considered in detail themselves and are subsequently compared with a multi-layered perceptron (MLP), in terms of performance and usage.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:21649
Uncontrolled Keywords:multi-layered perceptron, nonlinear system identification, performance, radial basis functions, usage
Additional Information:Proceedings ISBN: 0780335902

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