Dynamics of state controlled CNNs

In this paper a new structure of Cellular Neural Network, called State-Controlled Cellular Neural Network (SC-CNN), is introduced. Such an architecture allows to exactly generate a wide class of nonlinear dynamical systems by a suitable connection of SC-CNN cells, which can be considered as analog primitives to generate very complex dynamics, including chaos and hyperchaos. This paper investigates about the dynamics of SC-CNNs from a theoretical point of view: the boundness of the state variables is proven and sufficient conditions on the state template are found in order to guarantee such a structure to admit stable equilibrium points in suitable regions of the state space