NDLI: Research on Overcoming the Local Optimum of BPNN

Content Provider	IEEE Xplore Digital Library
Author	Zhen Li Lingyu Xu
Copyright Year	2006
Description	Author affiliation: Sch. of Comput. Eng. & Sci., Shanghai Univ. (Zhen Li; Lingyu Xu)
Abstract	It's difficult to satisfy the practical applications for BPNN (back propagation neural network), which has some vital drawbacks including learning slowly and evolving into some local optimum solutions early etc. There are two fundamental reasons which lead to these drawbacks. One reason is that BPNN is too mechanical and elementary to generalize from training experience for lack of advanced intelligent characteristic. The other lies in that BP can not overcome the symptoms including fatigue and overload produced in the process of learning without any powerful mechanism. In order to generalize from training experience and detect-correct the symptomatic neurons, PEBP (phase evaluation BP) is proposed with the phase evaluation mechanism by dividing the process of training into a certain number of phases. At the end of each phase, PEBP evaluate some statistic of current phase and whole training process, then regulate current learning mode and rectify the symptomatic neurons. Compared with the traditional BP, two kinds of PEBP show higher performance from the result of simulated prediction on the same test data set
Starting Page	2681
Ending Page	2685
File Size	315604
Page Count	5
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1712850
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Neurons Computer networks Electronic mail Application software Fatigue Phase detection Statistics Predictive models Testing local optimum BP neural network supersaturation
Content Type	Text
Resource Type	Article

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