A dynamic k-winners-take-all neural network

Jar Ferr Yang, Chi Ming Chen

研究成果: Article同行評審

8 引文 斯高帕斯(Scopus)

摘要

In this paper, a dynamic k-winners-take-all (KWTA) neural network, which can quickly identify the K-winning neurons whose activations are larger than the remaining ones, is proposed and analyzed. For N competitors, the proposed KWTA network is composed of N feedforward hardlimit neurons and three feedback neurons, which are used to determine the dynamic threshold. From theoretical analysis and simulation results, we found that the convergence of the proposed KWTA network, which requires Log2 (N + 1) iterations in average to complete a KWTA process, is independent of K, the number of the desired winners, and faster than that of the existing KWTA networks.

原文English
頁(從 - 到)523-526
頁數4
期刊IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
27
發行號3
DOIs
出版狀態Published - 1997

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 軟體
  • 資訊系統
  • 人機介面
  • 電腦科學應用
  • 電氣與電子工程

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