A hybrid forward algorithm for RBF neural network construction.

Jian-Xun Peng; Kang Li; De-Shuang Huang

doi:10.1109/tnn.2006.880860

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A hybrid forward algorithm for RBF neural network construction.

Jian-Xun Peng·Kang Li·De-Shuang Huang

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Abstract

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness.

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Algorithms Information Storage and Retrieval Neural Networks, Computer Pattern Recognition, Automated Signal Processing, Computer-Assisted Journal Article Research Support, Non-U.S. Gov't

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Jian-Xun Peng, Kang Li, De-Shuang Huang. 2006. A hybrid forward algorithm for RBF neural network construction.. https://doi.org/10.1109/tnn.2006.880860

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