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Analysis and Design of a k -Winners-Take-All Model With a Single State Variable and the Heaviside Step Activation Function

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1 Author(s)
Jun Wang ; Dept. of Mech. & Autom. Eng., Chinese Univ. of Hong Kong, Hong Kong, China

This paper presents a k-winners-take-all (kWTA) neural network with a single state variable and a hard-limiting activation function. First, following several kWTA problem formulations, related existing kWTA networks are reviewed. Then, the kWTA model model with a single state variable and a Heaviside step activation function is described and its global stability and finite-time convergence are proven with derived upper and lower bounds. In addition, the initial state estimation and a discrete-time version of the kWTA model are discussed. Furthermore, two selected applications to parallel sorting and rank-order filtering based on the kWTA model are discussed. Finally, simulation results show the effectiveness and performance of the kWTA model.

Published in:

Neural Networks, IEEE Transactions on  (Volume:21 ,  Issue: 9 )