Lu, Yingwei; Sundararajan, N.; Saratchandran, P. A sequential learning scheme for function approximation using minimal radial basis function neural networks. (English) Zbl 1067.68586 Neural Comput. 9, No. 2, 461-478 (1997). Summary: 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. Cited in 30 Documents MSC: 68T05 Learning and adaptive systems in artificial intelligence PDF BibTeX XML Cite \textit{Y. Lu} et al., Neural Comput. 9, No. 2, 461--478 (1997; Zbl 1067.68586) Full Text: DOI References: [1] DOI: 10.1006/dspr.1994.1016 [2] DOI: 10.1049/ip-d.1993.0058 · Zbl 0799.68161 [3] DOI: 10.1162/neco.1993.5.6.954 [4] DOI: 10.1162/neco.1989.1.2.281 [5] DOI: 10.1016/S0893-6080(05)80038-3 [6] DOI: 10.1162/neco.1991.3.2.213 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.