A mended hybrid learning algorithm for radial basis function neural networks to improve generalization capability

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Abstract

A two-step learning scheme for radial basis function neural networks, which combines the genetic algorithm (GA) with the hybrid learning algorithm (HLA), is proposed in this paper. It is compared with the methods of the GA, the recursive orthogonal least square algorithm (ROLSA) and another two-step learning scheme for RBF neural networks, which combines the K-means clustering with the HLA (K-means + HLA). Our proposed method has the best generalization performance. © 2006 Elsevier Inc. All rights reserved.

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Zhao, Z. Q., & Huang, D. S. (2007). A mended hybrid learning algorithm for radial basis function neural networks to improve generalization capability. Applied Mathematical Modelling, 31(7), 1271–1281. https://doi.org/10.1016/j.apm.2006.04.014

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