A sequential learning scheme for function approximation using minimal radial basis function neural networks.
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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School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
Sundararajan
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Saratchandran
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D000465:Algorithms; D007858:Learning; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009474:Neurons; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D015203:Reproducibility of Results