ANALOG-DIGITAL SELF-LEARNING FUZZY SPIKING NEURAL NETWORK AND ITS LEARNING ALGORITHM BASED ON` WINNER-TAKES-MORE' RULE

被引：0

作者：

Bodyanskiy, Ye. ^{[1
]}

Dolotov, A. ^{[1
]}

机构：

[1] Kharkov Natl Univ Radio Elect, Kharkov, Ukraine

来源：

RADIO ELECTRONICS COMPUTER SCIENCE CONTROL | 2010年 / 1卷

关键词：

analog-digital architecture; self-learning fuzzy spiking neural network; automatic; control theory; unsupervised learning algorithm; 'winner-takes-more'; rule;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Analog-digital architecture of self-learning fuzzy spiking neural network is proposed in this paper. Spiking neuron synapse and some are treated in terms of classical automatic control theory. Conventional unsupervised learning algorithm of spiking neural network is improved by applying `Winner-Takes-More' rule.

引用

页码：109 / 116

页数：8

共 16 条

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[3] Bodyanskiy Ye, 2009, ADAPTIVE GUSTAFSON K, P17
[4] Bodyanskiy Ye, 2009, P INT C INT SYST DEC, V2, P154
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[6] A STOCHASTIC-MODEL OF RETINOTOPY - A SELF-ORGANIZING PROCESS
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[J]. BIOLOGICAL CYBERNETICS, 1986, 53 (06) : 405 - 411
[7] Dolotov A., 2008, SCI P RIG TU INF TEC, V36, P27
[8] Gerstner W., 2002, SPIKING NEURON MODEL
[9] Haykin S., 1999, NEURAL NETWORKS COMP
[10] JANG JR, 1997, COMPUTATIONAL APPROA

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