A sequential learning scheme for function approximation using minimal radial basis function neural networks.

Y Lu; N Sundararajan; P Saratchandran

doi:10.1162/neco.1997.9.2.461

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A sequential learning scheme for function approximation using minimal radial basis function neural networks.

Y Lu·N Sundararajan·P Saratchandran

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Abstract

This article presents a sequential learning algorithm for function approximation and time-series prediction using a minimal radial basis function neural network (RBFNN). The algorithm combines the growth criterion of the resource-allocating network (RAN) of Platt (1991) with a pruning strategy based on the relative contribution of each hidden unit to the overall network output. The resulting network leads toward a minimal topology for the RBFNN. The performance of the algorithm is compared with RAN and the enhanced RAN algorithm of Kadirkamanathan and Niranjan (1993) for the following benchmark problems: (1) hearta from the benchmark problems database PROBEN1, (2) Hermite polynomial, and (3) Mackey-Glass chaotic time series. For these problems, the proposed algorithm is shown to realize RBFNNs with far fewer hidden neurons with better or same accuracy.

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Y Lu, N Sundararajan, P Saratchandran. 1997-02-15. A sequential learning scheme for function approximation using minimal radial basis function neural networks.. https://doi.org/10.1162/neco.1997.9.2.461

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