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Stochastic stability analysis for neutral-type Markov jump neural networks with additive time-varying delays via a new reciprocally convex combination inequality

Author

Listed:
  • Haiyang Zhang
  • Zhipeng Qiu
  • Lianglin Xiong
  • Guanghao Jiang

Abstract

This paper investigates the stochastic stability problem for a class of neutral-type Markov jump neural networks with additive time-varying delays. Firstly, to derive a tighter lower bound of the reciprocally convex quadratic terms, a new reciprocally convex combination inequality is established by using parameters transformation approach. Secondly, by fully considering the peculiarity of various time-varying delays and Markov jumping parameters, an eligible stochastic Lyapunov–Krasovskii functional is constructed. Then, by employing the new reciprocally convex combination inequality and other analytical techniques, some novel stability criteria are provided in the forms of linear matrix inequalities. Finally, four illustrated examples are given to verify the effectiveness and feasibility of the proposed methods.

Suggested Citation

  • Haiyang Zhang & Zhipeng Qiu & Lianglin Xiong & Guanghao Jiang, 2019. "Stochastic stability analysis for neutral-type Markov jump neural networks with additive time-varying delays via a new reciprocally convex combination inequality," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(5), pages 970-988, April.
    • Handle: RePEc:taf:tsysxx:v:50:y:2019:i:5:p:970-988
    DOI: 10.1080/00207721.2019.1586005
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