Title :
A Novel Recurrent Neural Network With One Neuron and Finite-Time Convergence for
-Winners-Take-All Operation
Author :
Liu, Qingshan ; Dang, Chuangyin ; Cao, Jinde
Author_Institution :
Sch. of Autom., Southeast Univ., Nanjing, China
fDate :
7/1/2010 12:00:00 AM
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.
Keywords :
Lyapunov matrix equations; linear programming; recurrent neural nets; Lyapunov method; finite time convergence; k -WTA; k-winners take-all operation; linear programming problem; recurrent neural network; $k$-winners-take-all operation; Lyapunov function; global convergence in finite time; recurrent neural network; Algorithms; Animals; Feedback; Game Theory; Models, Neurological; Neural Networks (Computer); Neurons; Time Factors;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2010.2050781