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Passivity of Memristive BAM Neural Networks with Probabilistic and Mixed Time-Varying Delays

Author

Listed:
  • Weiping Wang
  • Meiqi Wang
  • Xiong Luo
  • Lixiang Li
  • Wenbing Zhao

Abstract

This paper is concerned with the passivity problem of memristive bidirectional associative memory neural networks (MBAMNNs) with probabilistic and mixed time-varying delays. By applying random variables with Bernoulli distribution, the information of probability time-varying delays is taken into account. Furthermore, we consider the probability distribution of the variation and the extent of the delays; therefore, the results derived are less conservative than in the existing papers. In particular, the leakage delays as well as distributed delays are all taken into consideration. Based on appropriate Lyapunov-Krasovskii functionals (LKFs) and some useful inequalities, several conditions for passive performance are established in linear matrix inequalities (LMIs). Finally, numerical examples are given to demonstrate the feasibility of the presented theories, and the results reveal that the probabilistic and mixed time-varying delays have an unstable influence on the system and should not be ignored.

Suggested Citation

  • Weiping Wang & Meiqi Wang & Xiong Luo & Lixiang Li & Wenbing Zhao, 2018. "Passivity of Memristive BAM Neural Networks with Probabilistic and Mixed Time-Varying Delays," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-25, April.
    • Handle: RePEc:hin:jnlmpe:5830160
    DOI: 10.1155/2018/5830160
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