A dynamic K-winners-take-all neural network

作者：Jar‐Ferr Yang, Chi-Ming Chen · 发表于：IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 年份：1997 · DOI：10.1109/3477.584959 · 被引用次数：18 · 研究领域：Neural Networks and Applications、Machine Learning and ELM、Blind Source Separation Techniques

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 Log(2)(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.