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Improved delay-probability-dependent results for stochastic neural networks with randomly occurring uncertainties and multiple delays

Author

Listed:
  • Jinnan Luo
  • Wenhong Tian
  • Shouming Zhong
  • Kaibo Shi
  • Xian-Ming Gu
  • Wenqin Wang

Abstract

This study seeks to address the delay-probability-dependent stability problem for a new class of stochastic neural networks with randomly occurring uncertainties, neutral type delay, distributed delay and probability-distribution delay. The system not only includes the randomly occurring uncertainties of parameters (ROUPs) but also contains stochastic disturbances, which is not yet investigated in existing papers. First, several stochastic variables which obey Bernoulli distribution are introduced to describe the ROUPs, based on which a new model is built. Second, through fully considering the information on kinds of delays and utilising general delay-partitioning method, an improved Lyapunov–Krasovskii function (LKF) is constructed. Combining Itô's differential formula, general bounding, free-weighting matrix and stochastic methods, a new delay-probability-dependent robustly mean square stable criterion is formulated in terms of linear matrix inequality. Finally, two numerical examples are given to demonstrate the effectiveness of the proposed results.

Suggested Citation

  • Jinnan Luo & Wenhong Tian & Shouming Zhong & Kaibo Shi & Xian-Ming Gu & Wenqin Wang, 2018. "Improved delay-probability-dependent results for stochastic neural networks with randomly occurring uncertainties and multiple delays," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(9), pages 2039-2059, July.
    • Handle: RePEc:taf:tsysxx:v:49:y:2018:i:9:p:2039-2059
    DOI: 10.1080/00207721.2018.1483044
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