SYNCHRONIZATION OF CHAOTIC NEURAL NETWORKS AND APPLICATIONS TO COMMUNICATIONS

Abstract

Methods for synchronizing discrete time chaotic neural networks are presented with possible applications in single- or multi-user private communications. Chaotic neurons, characterized with a piecewise-linear N-shaped transfer function, are connected into Hopfield-like networks with parameters set for chaos. The networks are used as transmitter and receiver circuits in chaotic communications schemes. The first algorithm is a modification of simple chaotic masking which makes synchronization robust and insensitive to the perturbation from the added information signal. A mathematical proof and simulation results of the scheme are shown for small networks. We have verified the method experimentally, using single- and two-neuron circuits. The second algorithm utilizes modulation of the transmitting chaotic network by a binary bit stream and detection of the corresponding synchronization error at the receiver. A method for multiple-user chaotic communication is also presented, utilizing chaotic neurons and spread spectrum techniques. The effects of additive noise in the proposed communication schemes are considered and simulated. Synchronization of larger networks and possible applications are also discussed.