Controllability of time-varying cellular neural networks

Abstract

In this work, we consider the model of Cellular Neural Network (CNN) introduced by Chua and Yang in 1988, but with the cloning templates ω-periodic in time. By imposing periodic boundary conditions the matrices involved in the system become circulant and ω-periodic. We show some results on the controllability of the linear model using a Theorem by Brunovsky for the case of linear and ω-periodic system. Also we use this approach in image detection, specifically foreground, background and contours of figures in different scales of grey.