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