Dynamic properties of Cellular Neural Networks

Dynamic behavior of a new class of information-processing systems called Cellular Neural Networks is investigated. This paper introduces a small parameter in the state equation of a cellular neural network and it seeks for periodic phenomena. New approach is used for proving stability of a cellular neural network by constructing Lyapunov’s majorizing equations. This algorithm is helpful for finding a map from initial continuous state space of a cellular neural network into discrete output. A comparison between cellular neural networks and cellular automata is made.