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Synchronization of Semi-Markovian Jump Neural Networks with Randomly Occurring Time-Varying Delays

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  • Mengping Xing
  • Hao Shen
  • Zhen Wang

Abstract

Based on the Lyapunov stability theory, this paper mainly investigates the synchronization problem for semi-Markovian jump neural networks (semi-MJNNs) with randomly occurring time-varying delays (TVDs). The continuous-time semi-MJNNs, where the transition rates are dependent on sojourn time, are introduced to make the issue under our consideration more general. One of the main characteristics of our work is the handling of TVDs. In addition to using the improved Jensen inequality and the reciprocal convexity lemma to deal with the integral inequality, we also employ Schur complement and the projection lemma to achieve the decoupling between the square term of TVDs. Finally, we verify the validity and feasibility of our method by a couple of simulation examples.

Suggested Citation

  • Mengping Xing & Hao Shen & Zhen Wang, 2018. "Synchronization of Semi-Markovian Jump Neural Networks with Randomly Occurring Time-Varying Delays," Complexity, Hindawi, vol. 2018, pages 1-16, September.
    • Handle: RePEc:hin:complx:8094292
    DOI: 10.1155/2018/8094292
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    Cited by:

    1. Wang, Xuelian & Xia, Jianwei & Wang, Jing & Wang, Jian & Wang, Zhen, 2019. "Passive state estimation for fuzzy jumping neural networks with fading channels based on the hidden Markov model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    2. Ziye Zhang & Xiaoping Liu & Chong Lin & Bing Chen, 2018. "Finite-Time Synchronization for Complex-Valued Recurrent Neural Networks with Time Delays," Complexity, Hindawi, vol. 2018, pages 1-14, December.
    3. Tao, Ruifeng & Ma, Yuechao & Wang, Chunjiao, 2020. "Stochastic admissibility of singular Markov jump systems with time-delay via sliding mode approach," Applied Mathematics and Computation, Elsevier, vol. 380(C).

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