Improving the robustness of winner-take-all cellular neural networks

This paper describes two improvements on a recently proposed winner-take-all (WTA) architecture with linear circuit complexity based on the cellular neural network paradigm. The general design technique originally used to select parameter values is extended to allow values to be optimized for robustness against relative parameter variations as well as absolute variations. In addition, a modified architecture, called clipped total feedback winner-take-all (CTF-WTA) is proposed. This architecture is shown to share most properties of standard cellular neural networks, but is shown to be better suited to the WTA application. It is shown to be less sensitive to parameter variations and under some conditions to converge faster than the standard cellular version. In addition, the effect of asymmetry between the neurons on the reliability of the circuit is examined, and CTF-WTA is found to be superior