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.
2008
9781424420902
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].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/2579
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