Exponential convergence rate estimation for neutral BAM neural networks with mixed time-delays

  • Authors:

  • Bo Chen;Li Yu;Wen-An Zhang

  • Affiliations:

  • Zhejiang University of Technology, College of Information Engineering, 310023, Hangzhou, People’s Republic of China and Zhejiang Provincial United Key Laboratory of Embedded Systems, 310023 ...;Zhejiang University of Technology, College of Information Engineering, 310023, Hangzhou, People’s Republic of China and Zhejiang Provincial United Key Laboratory of Embedded Systems, 310023 ...;Zhejiang University of Technology, College of Information Engineering, 310023, Hangzhou, People’s Republic of China and Zhejiang Provincial United Key Laboratory of Embedded Systems, 310023 ...

  • Venue:
  • Neural Computing and Applications
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper is concerned with the exponential stability analysis problem for a class of neutral bidirectional associative memory neural networks with mixed time-delays, where discrete, distributed and neutral delays are involved. By utilizing the delay decomposition approach and an appropriately constructed Lyapunov–Krasovskii functional, some novel delay-dependent and decay rate-dependent criteria for the exponential stability of the considered neural networks are derived and presented in terms of linear matrix inequalities. Furthermore, the maximum allowable decay rate can be estimated based on the obtained results. Three numerical examples are given to demonstrate the effectiveness of the proposed method.