A General Mean-Based Iterative Winner-Take-All Neural Network

Jar Ferr Yang; Chi Ming Chen; Wen Chung Wang; Jau Yien Lee

doi:10.1109/72.363454

A General Mean-Based Iterative Winner-Take-All Neural Network

Jar Ferr Yang, Chi Ming Chen, Wen Chung Wang, Jau Yien Lee

Institute of Computer and Communication Engineering

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

18 Citations (Scopus)

Abstract

In this paper, a new iterative winner-take-all (WTA) neural network is developed and analyzed. The proposed WTA neural net with one-layer structure is established under the concept of the statistical mean. For three typical distributions of initial activations, the convergence behaviors of the existing and the proposed WTA neural nets are evaluated by theoretical analyses and Monte Carlo simulations. We found that the suggested WTA neural network on average requires fewer than Log2 M iterations to complete a WTA process for the three distributed inputs, where M is the number of competitors. Furthermore, the fault tolerances of the iterative WTA nets are analyzed and simulated. From the view points of convergence speed, hardware complexity, and robustness to the errors, the proposed WTA is suitable for various applications.

Original language	English
Pages (from-to)	14-24
Number of pages	11
Journal	IEEE Transactions on Neural Networks
Volume	6
Issue number	1
DOIs	https://doi.org/10.1109/72.363454
Publication status	Published - 1995 Jan

All Science Journal Classification (ASJC) codes

Software
Computer Science Applications
Computer Networks and Communications
Artificial Intelligence

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abstract = "In this paper, a new iterative winner-take-all (WTA) neural network is developed and analyzed. The proposed WTA neural net with one-layer structure is established under the concept of the statistical mean. For three typical distributions of initial activations, the convergence behaviors of the existing and the proposed WTA neural nets are evaluated by theoretical analyses and Monte Carlo simulations. We found that the suggested WTA neural network on average requires fewer than Log2 M iterations to complete a WTA process for the three distributed inputs, where M is the number of competitors. Furthermore, the fault tolerances of the iterative WTA nets are analyzed and simulated. From the view points of convergence speed, hardware complexity, and robustness to the errors, the proposed WTA is suitable for various applications.",

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