A neural network model based on BP learning with stochastic resonance

被引：0

作者：

Matsui, N ^{[1
]}

Fujiwara, K ^{[1
]}

Isokawa, T ^{[1
]}

机构：

[1] Himeji Inst Technol, Dept Comp Engn, Himeji, Hyogo 6712201, Japan

来源：

ICONIP'98: THE FIFTH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING JOINTLY WITH JNNS'98: THE 1998 ANNUAL CONFERENCE OF THE JAPANESE NEURAL NETWORK SOCIETY - PROCEEDINGS, VOLS 1-3 | 1998年

关键词：

stochastic resonance; BP learning; SR neural network; local minima; periodic signal; SNR;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We describe the results of computer simulations of the dynamical behavior of a neural network model based on BP learning incorporated stochastic resonance. Such a model can acquire the ability of the avoidance of local minima. Our results show that, under the influence of a weak periodic signal, the network exhibits a maximum in the signal to noise ratio at an optimum noise level: the characteristic signature of stochastic resonance. At this level, the learning abilities of our network indicate a significant improvement compared to the limited abilities of the traditional BP learning neural network.

引用

页码：1579 / 1582

页数：4

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