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Proceedings Paper

Hierarchical structure stochastic automata can increase the efficiency of the back propagation method with momentum
Author(s): Norio Baba; Hisashi Handa
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Paper Abstract

One of the most remarkable breakthroughs in the field of neural networks may be the invention of the back propagation (BP) method by Rumelhart et al. The back propagation method has so far contributed a lot in constructing various useful intelligent systems. However, the original BP method involves several limitations. One of the most important is that it sometimes falls into a local minimum without being able to find a global minimum of the total error function. In order to overcome limitations of the original BP method, various modified BP methods have been proposed so far. The BP method with momentum may be one of the most popular modified algorithms. It has been reported that the BP method with momentum has been applied quite successfully to various actual problems. However, despite its effectiveness, this method involves the following serious problem: `Its learning performance depends heavily upon the selection of the value of the momentum factor'. Unfortunately, it seems that there has not so far been proposed an intelligent algorithm for determining an appropriate value of the momentum factor. In this paper, we suggest that hierarchical structure stochastic automata are quite helpful for finding an appropriate value of the momentum factor of the BP method with momentum. Several computer simulation results confirm our suggestion.

Paper Details

Date Published: 22 March 1996
PDF: 11 pages
Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235957
Show Author Affiliations
Norio Baba, Osaka Kyoiku Univ. (Japan)
Hisashi Handa, Osaka Kyoiku Univ. (Japan)


Published in SPIE Proceedings Vol. 2760:
Applications and Science of Artificial Neural Networks II
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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