288 pp. per issue
6 x 9, illustrated
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Neural Computation

February 15, 1997, Vol. 9, No. 2, Pages 461-478
(doi: 10.1162/neco.1997.9.2.461)
© 1997 Massachusetts Institute of Technology
A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural Networks
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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.