TAILIEUCHUNG - Báo cáo hóa học: " Research Article Extended LaSalle’s Invariance Principle for Full-Range Cellular Neural Networks"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Extended LaSalle’s Invariance Principle for Full-Range Cellular Neural Networks | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 730968 10 pages doi 2009 730968 Research Article Extended LaSalle s Invariance Principle for Full-Range Cellular Neural Networks Mauro Di Marco Mauro Forti Massimo Grazzini and Luca Pancioni Department of Information Engineering University of Siena 53100 - Siena Italy Correspondence should be addressed to Mauro Di Marco dimarco@ Received 15 September 2008 Accepted 20 February 2009 Recommended by Diego Cabello Ferrer In several relevant applications to the solution of signal processing tasks in real time a cellular neural network CNN is required to be convergent that is each solution should tend toward some equilibrium point. 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 CNNs. The applicability of the method is demonstrated by obtaining 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. Copyright 2009 Mauro Di Marco et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. Introduction The Full-Range FR model of cellular neural networks CNNs has been introduced in 1 in order to obtain advantages in the VLSI implementation of CNN chips with a large number of neurons. One main feature is the use of hard-limiter nonlinearities that constrain the evolution of the FR-CNN trajectories within a closed hypercube of the state space. This improved range of the trajectories has enabled us to reduce the power .

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