Method and Application of RBF Network Structure Optimization

Abstract:

The hidden unit number of RBF neural networks directly influences the performances of the whole net. A new strategy to prune the hidden units based on the singular value decomposition (SVD) of matrixes is proposed in the paper. At the basis of a structure involving enough more hidden units, the paper analyzes the outputs corresponding to some training samples with the SVD method and finds out the internal relations of them, then removes redundant ones according to the contribution rate of every hidden unit to the whole network, simplifies the structure of RBF neural network at last. The optimized network has strong generalization ability with simpler structure. At the end of this paper the new strategy is successfully used in the main steam system modeling of power plant and confirmed by simulation experiments.

• Periodical: Advanced Materials Research (Volumes 472-475)
• Pages: 1668-1675
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.472-475.1668
• Online since: February 2012
• Authors: Zi Bo Han, Jin Fang Yang
• Keywords: Main Steam System Modeling, RBF Neural Network, Singular Value Decomposition (SVD), Structure Optimization

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