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A Novel Recurrent Neural Network With One Neuron and Finite-Time Convergence for k -Winners-Take-All Operation

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3 Author(s)
Qingshan Liu ; School of Automation, Southeast University, Nanjing, China ; Chuangyin Dang ; Jinde Cao

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.

Published in:

IEEE Transactions on Neural Networks  (Volume:21 ,  Issue: 7 )