Edge-of-Chaos in CNN Models with Memristor Synapses

In this chapter, analytical results are derived for CNN models with memristor synapses (M-CNN) in which neurons operate in a regime called edge-of-chaos. The systems describing the models under consideration consist of highly nonlinear differential equations. We propose new algorithms based on the generalized local activity scheme for the determination of the edge-of-chaos regime in M-CNN. MATLAB implementation of algorithms based on a numerical integration of the M-CNN state equations allowing a reliable and accurate determination of the edge-of-chaos parameter regime is proposed. Applications of the obtained results for noise removing are presented. New M-CNN model arising in nano-structures is proposed. The model consists of 2D dynamic coupled problem in multifunctional nano-heterogeneous piezoelectric composites. Simulations and validation are presented for transversely isotropic piezoelectric material PZT4.