BP neural network optimization based on an improved genetic algorithm

被引：0
|
作者：
Yang, B [1 ]
Su, XH [1 ]
Wang, YD [1 ]
机构：
[1] Harbin Inst Technol, Sch Comp Sci & Engn, Harbin 150001, Peoples R China
来源：
2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS | 2002年
关键词：
evolutionarily stable strategy; genetic algorithm; neural network; back propagation (BP) algorithm; premature convergence;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An improved Genetic Algorithm based on Evolutionarily Stable Strategy is proposed to optimize the initial weights of BP network in this paper. The improvement of GA lies in the introducing of a new mutation operator under control of a stable factor, which is found to be a very simple and effective searching operator. The experimental results in BP neural network optimization show that this algorithm can effectively avoid BP network converging to local optimum. It is found by comparison that the improved, genetic algorithm can almost avoid the trap of local optimum and effectively improve the convergent speed.
引用
收藏
页码：64 / 68
页数：5
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