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Proceedings Paper

Time series prediction with a radial basis function neural network
Author(s): Michael A. S. Potts; David S. Broomhead
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Paper Abstract

The radial basis function network is implemented in an iterative form for the prediction of time series by modeling their generating dynamics. The technique is demonstrated on an experimental time series, for which the iterated network learns an attracting solution. Analysis of the Lyapunov exponents and their local analogs reveals the presence of local instability while giving insight into how overall stability is achieved.

Paper Details

Date Published: 1 December 1991
PDF: 12 pages
Proc. SPIE 1565, Adaptive Signal Processing, (1 December 1991); doi: 10.1117/12.49782
Show Author Affiliations
Michael A. S. Potts, Royal Signals & Radar Establishment (United Kingdom)
David S. Broomhead, Royal Signals & Radar Establishment (United Kingdom)


Published in SPIE Proceedings Vol. 1565:
Adaptive Signal Processing
Simon Haykin, Editor(s)

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