An introduction to radial basis functions for system identification: a comparison with other neural network methodsWarwick, 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. Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. Official URL: http://dx.doi.org/10.1109/CDC.1996.574355 Abstract/SummaryA 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.
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