Robust k-WTA Network Generation, Analysis, and Applications to Multiagent Coordination.

In this article, a robust k-winner-take-all (k-WTA) neural network employing the saturation-allowed activation functions is designed and investigated to perform a k-WTA operation, and is shown to possess enhanced robustness to disturbance compared to existing k-WTA neural networks. Global convergence and robustness of the proposed k-WTA neural network are demonstrated through analysis and simulations. An application studied in detail is competitive multiagent coordination and dynamic task allocation, in which k active agents [among m (m > k)] are allocated to execute a tracking task with the static m-k ones. This is implemented by adopting a distributed k-WTA network with limited communication, aided with a consensus filter. Simulation results demonstrating the system's efficacy and feasibility are presented.