Dynamics for Discrete-Time Cellular Neural Networks

摘要: This presentation investigates the dynamics of discrete-time cellular neural networks (DT-CNN). In contrast to classical that are mostly gradient-like systems, DT-CNN possesses both complete stability and chaotic behaviors as different parameters considered. An energy-like function which decreases along orbits well existence a globally attracting set derived. Complete can then be concluded, with further analysis on sets energy is constant. The formations saturated stationary patterns for shown analogous ones in continuous-time CNN. Thus, shares similar properties By confirming snap-back repellers, hence transversal homoclinic orbits, we also conclude certain exhibits dynamics, according theorem by Marotto.