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

Authors:
Lei Zhang;Pheng Ann Heng;Zhang Yi
Affiliations:
Department of Computer Science and Engineering, The Chinese University of Hong Kong,Shatin, Hong Kong;Department of Computer Science and Engineering, The Chinese University of Hong Kong,Shatin, Hong Kong and School of Computer Science and Engineering, University of Electronic Science and Technolog ...;Machine Intelligence Laboratory, School of Computer Science, Sichuan University, Chengdu, P.R. China 610065
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

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Abstract

The competitive layer model (CLM) can be 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 be used to implement the CLM.