Sequential Adaptation of Radial Basis Function Neural Networks and its Application to Time-series Prediction

Part of Advances in Neural Information Processing Systems 3 (NIPS 1990)

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V. Kadirkamanathan, M. Niranjan, F. Fallside


We develop a sequential adaptation algorithm for radial basis function (RBF) neural networks of Gaussian nodes, based on the method of succes(cid:173) sive F-Projections. This method makes use of each observation efficiently in that the network mapping function so obtained is consistent with that information and is also optimal in the least L 2-norm sense. The RBF network with the F-Projections adaptation algorithm was used for pre(cid:173) dicting a chaotic time-series. We compare its performance to an adapta(cid:173) tion scheme based on the method of stochastic approximation, and show that the F-Projections algorithm converges to the underlying model much faster.