H∞ Filtering in Neural Network Training and Pruning with Application to System Identification

An efficient training and pruning methodology based on the H∞ filtering algorithm is proposed for artificial neural networks (ANNs). ANNs are first trained by the H∞ filtering algorithm and then some unimportant weights are removed based on the training. The results presented in the paper show that...

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Bibliographic Details
Published in:Journal of computing in civil engineering 2007-01, Vol.21 (1), p.47-58
Main Authors: Tang, He-Sheng, Xue, Songtao, Sato, Tadanobu
Format: Article
Language:English
Subjects:
Applied sciences
Buildings. Public works
Computation methods. Tables. Charts
Exact sciences and technology
Structural analysis. Stresses
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Summary:An efficient training and pruning methodology based on the H∞ filtering algorithm is proposed for artificial neural networks (ANNs). ANNs are first trained by the H∞ filtering algorithm and then some unimportant weights are removed based on the training. The results presented in the paper show that the proposed method provides better pruning results of the network without losing its generalization capacity. It also provides a robust training algorithm for given arbitrary network structures. The usefulness and effectiveness of the proposed methodology are demonstrated in developing an ANN model of a hysteretic structural system.
ISSN:0887-3801
1943-5487
DOI:10.1061/(ASCE)0887-3801(2007)21:1(47)