Delay-Dependent Exponential Stability of Discrete-Time BAM Neural Networks with Time Varying Delays

  • Authors:

  • Rui Zhang;Zhanshan Wang;Jian Feng;Yuanwei Jing

  • Affiliations:

  • School of Information Science and Engineering, Northeastern University, Shenyang, China 110004;School of Information Science and Engineering, Northeastern University, Shenyang, China 110004;School of Information Science and Engineering, Northeastern University, Shenyang, China 110004;School of Information Science and Engineering, Northeastern University, Shenyang, China 110004

  • Venue:
  • ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
  • Year:
  • 2009

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Abstract

In this paper, the global exponential stability is discussed for discrete-time bidirectional associative memory (BAM) neural networks with time varying delays. By the linear matrix inequality (LMI) technique and discrete Lyapunov functional combined with inequality techniques, a new global exponential stability criterion of the equilibrium points is obtained for this system. The proposed result is less restrictive than those given in the earlier literatures, and easier to check in practice. Remarks are made with other previous works to show the superiority of the obtained results, and the simulation example is used to demonstrate the effectiveness of our result.