DocumentCode
1545073
Title
A dynamic K-winners-take-all neural network
Author
Yang, Jar-Ferr ; Chen, Chi-Ming
Author_Institution
Dept. of Electr. Eng., Cheng Kung Univ., Tainan, Taiwan
Volume
27
Issue
3
fYear
1997
fDate
6/1/1997 12:00:00 AM
Firstpage
523
Lastpage
526
Abstract
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
Keywords
digital simulation; feedback; neural nets; K-winning neurons; dynamic K-winners-take-all neural network; dynamic threshold; feedback neurons; feedforward hardlimit neurons; simulation results; Associative memory; Clocks; Control systems; Convergence; Councils; Neural networks; Neurons; Resonance; Self organizing feature maps; Subspace constraints;
fLanguage
English
Journal_Title
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher
ieee
ISSN
1083-4419
Type
jour
DOI
10.1109/3477.584959
Filename
584959
Link To Document