Improved Global Robust Stability Criteria for Delayed BAM Neural Networks

  • Xiaolin Li
  • Ming Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7064)

Abstract

This paper is concerned with uniqueness and global robust stability for the equilibrium point of the interval bidirectional associative memory (BAM) delayed neural networks. By employing linear matrix inequality and Lyapunov functional, a new criterion is proposed for the global robust stability of BAM neural networks. An example is given to show the effectiveness of the present results.

Keywords

Equilibrium Point Linear Matrix Inequality Exponential Stability Global Asymptotic Stability Bidirectional Associative Memory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Xiaolin Li
    • 1
  • Ming Liu
    • 1
  1. 1.Department of MathematicsShanghai UniversityShanghaiChina

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