Global asymptotic stability of Discrete-Time Cellular Neural Networks


Arik S. , Kilinc A., Savaci F.

5th IEEE International Workshop on Cellular Neural Networks and Their Applications, London, Canada, 14 - 17 April 1998, pp.52-55 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/cnna.1998.685329
  • City: London
  • Country: Canada
  • Page Numbers: pp.52-55

Abstract

This paper presents two sufficient conditions for global stability of Discrete-Time Cellular Neural Networks (DTCNNs). It is shown that if the first or second norm of the feedback matrix is smaller than one, then a DTCNN converges to a unique and globally asymptotically stable equilibrium point for every external input.