A hybrid learning algorithm combined with generalized RLS approach for radial basis function neural networks

• Published in: Applied mathematics and computation Vol. 205; no. 2; pp. 908 - 915
• Main Authors: Du, Ji-Xiang, Zhai, Chuan-Min
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Amsterdam Elsevier Inc 15.11.2008; Elsevier
• Subjects: Combinatorics; Combinatorics. Ordered structures; Designs and configurations; Exact sciences and technology; Generalized recursive least square mode; Hybrid learning; Mathematical analysis; Mathematics; Numerical analysis; Numerical analysis. Scientific computation; Radial basis function neural networks; Sciences and techniques of general use; Hybrid learning; Radial basis function neural networks; Generalized recursive least square mode; Learning; Experimental result; Numerical analysis; Applied mathematics; Least squares method; Neural network; Learning algorithm; Recursive algorithm
• ISSN: 0096-3003; 1873-5649
• DOI: 10.1016/j.amc.2008.05.075

In this paper, a new hybrid learning method for radial basis function neural networks based on generalized recursive least square algorithm is proposed. Firstly the generalized recursive least square (GRLS) model including a general quadratic weight decay term in the energy function for the training of RBF neural networks is described. Then combined with the GRLS approach, a new hybrid learning method is proposed to meet the design goals: improving the generalization ability of the trained network. Finally experimental results demonstrate that our approach can achieve a significantly improved generalization performance of the RBF networks.