1 December 1991 Time series prediction with a radial basis function neural network
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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.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael A. S. Potts, Michael A. S. Potts, David S. Broomhead, David S. Broomhead, } "Time series prediction with a radial basis function neural network", Proc. SPIE 1565, Adaptive Signal Processing, (1 December 1991); doi: 10.1117/12.49782; https://doi.org/10.1117/12.49782

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