Solving the CLM Problem by Discrete-Time Linear Threshold Recurrent Neural Networks

被引：0

作者：

Zhang, Lei ^{[1
]}

Heng, Pheng Ann ^{[1
,2
]}

Yi, Zhang ^{[3
]}

机构：

[1] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Shatin, Hong Kong, Peoples R China

[2] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 610054, Peoples R China

[3] Sichuan Univ, Sch Comp Sci, Machine Intelligence Lab, Chengdu 610065, Peoples R China

来源：

ARTIFICIAL NEURAL NETWORKS - ICANN 2009, PT I | 2009年 / 5768卷

关键词：

BINDING;

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：

The competitive layer model (CLM) can he described by the optimization problem that is formulated with the CLM energy function. The minimum points of CLM energy function can be achieved by running some proper recurrent neural networks. In other words, the CLM can be implemented by the recurrent neural networks. This paper proposes the discrete-time linear threshold recurrent networks to solve the CLM problem. The conditions for the stable attractors of the networks are obtained, which just correspond to the conditions of the minimum points of CLM energy function established in the literature before. Therefore, the proposed network can he used to implement the CLM.

引用

页码：995 / +

页数：2

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