A new k-winners-take-all neural network and its array architecture

Jui Cheng Yen; Jiun-In  Guo; Hun Chen Chen

doi:10.1109/72.712163

A new k-winners-take-all neural network and its array architecture

Jui Cheng Yen^*, Jiun-In Guo, Hun Chen Chen

^*Corresponding author for this work

Institute of Electronics

Research output: Contribution to journal › Article › peer-review

51 Scopus citations

Abstract

In this paper, a new neural-network model called WINSTRON and its novel array architecture are proposed. Based on a competitive learning algorithm that is originated from the coarse-fine competition, WINSTRON can identify the k larger elements or the k smaller ones in a data set. We will then prove that WINSTRON converges to the correct state in any situation. In addition, the convergence rates of WINSTRON for three special data distributions will be derived. In order to realize WINSTRON, its array architecture with low hardware complexity and high computing speed is also detailed. Finally, simulation results are included to demonstrate its effectiveness and its advantages over three existing networks.

Original language	English
Pages (from-to)	901-912
Number of pages	12
Journal	IEEE Transactions on Neural Networks
Volume	9
Issue number	5
DOIs	https://doi.org/10.1109/72.712163
State	Published - 1 Dec 1998

Keywords

Artificial neural network
Competitive learning
Distributed arithmetic
k-winners-take-all
Memory-based architecture
Systolic array

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