Extended LaSalle’s invariance principle for full-range cellular neural networks

The paper develops a Lyapunov method, which is based on a generalized version of LaSallepsilas invariance principle, for studying convergence and stability of the differential inclusions modeling the dynamics of the full-range (FR) model of cellular neural networks (CNNs). The method is applied to yield a rigorous proof of convergence for symmetric FR-CNNs. The proof, which is a direct consequence of the fact that a symmetric FR-CNN admits a strict Lyapunov function, is much more simple than the corresponding proof of convergence for symmetric standard CNNs.