Skip to content

Advertisement

  • Research Article
  • Open Access

Extended LaSalle's Invariance Principle for Full-Range Cellular Neural Networks

  • 1Email author,
  • 1,
  • 1 and
  • 1
EURASIP Journal on Advances in Signal Processing20092009:730968

https://doi.org/10.1155/2009/730968

  • Received: 15 September 2008
  • Accepted: 20 February 2009
  • Published:

Abstract

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.

Keywords

  • Direct Consequence
  • Information Technology
  • Signal Processing
  • Equilibrium Point
  • Quantum Information

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

(1)
Department of Information Engineering, University of Siena, 53100 Siena, Italy

Copyright

Advertisement