The paper develops a Lyapunov method, which is based on a generalized version of LaSalle's 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.
DI MARCO, M., Forti, M., Grazzini, M., Pancioni, L. (2008). Extended LaSalle’s invariance principle for full-range cellular neural networks. In Proceedings of IEEE 11th International Worshop on Cellular Neural Networks and their applications, 2008 (CNNA 2008) (pp.46-51) [10.1109/CNNA.2008.4588648].
Extended LaSalle’s invariance principle for full-range cellular neural networks
DI MARCO, MAURO;FORTI, MAURO;PANCIONI, LUCA
2008-01-01
Abstract
The paper develops a Lyapunov method, which is based on a generalized version of LaSalle's 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11365/2579
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