Analysis and design of a k -winners-take-all model with a single state variable and the heaviside step activation function.
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.
Wang
Jun
J
Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, Shatin, Hong Kong. jwang@mae.cuhk.edu.hk
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016571:Neural Networks, Computer; D012985:Software Design; D013997:Time Factors