Time series prediction with a radial basis function neural network

Michael A. S. Potts; David S. Broomhead

doi:10.1117/12.49782

1 December 1991 Time series prediction with a radial basis function neural network

Michael A. S. Potts, David S. Broomhead

Proceedings Volume 1565, Adaptive Signal Processing; (1991) https://doi.org/10.1117/12.49782
Event: San Diego, '91, 1991, San Diego, CA, United States

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.

Citation Download Citation

Michael A. S. Potts and David S. Broomhead "Time series prediction with a radial basis function neural network", Proc. SPIE 1565, Adaptive Signal Processing, (1 December 1991); https://doi.org/10.1117/12.49782

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