NDLI: A Novel Recurrent Neural Network With One Neuron and Finite-Time Convergence for <formula formulatype=inline><tex Notation=TeX>$k$</tex></formula>-Winners-Take-All Operation

Content Provider	IEEE Xplore Digital Library
Author	Qingshan Liu Chuangyin Dang Jinde Cao
Copyright Year	1990
Abstract	In this paper, based on a one-neuron recurrent neural network, a novel k-winners-take-all (k -WTA) network is proposed. Finite time convergence of the proposed neural network is proved using the Lyapunov method. The k-WTA operation is first converted equivalently into a linear programming problem. Then, a one-neuron recurrent neural network is proposed to get the kth or (k + 1)th largest inputs of the k-WTA problem. Furthermore, a k-WTA network is designed based on the proposed neural network to perform the k-WTA operation. Compared with the existing k-WTA networks, the proposed network has simple structure and finite time convergence. In addition, simulation results on numerical examples show the effectiveness and performance of the proposed k-WTA network.
Sponsorship	IEEE Computational Intelligence Society
Page Count	9
File Size	457166
Starting Page	1140
Ending Page	1148
File Format	PDF
ISSN	10459227
Volume Number	21
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks Neurons Convergence Neural networks Lyapunov method Integrated circuit interconnections Linear programming Numerical simulation Associative memory recurrent neural network $k$-winners-take-all operation global convergence in finite time Lyapunov function
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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