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Extended dissipative estimator design for uncertain switched delayed neural networks via a novel triple integral inequality

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  • Xie, Wenqian
  • Zhu, Hong
  • Zhong, Shouming
  • Zhang, Dian
  • Shi, Kaibo
  • Cheng, Jun

Abstract

This paper addresses the problem of extended dissipative estimator design for uncertain switched neural networks (SNNs) with mixed time-varying delays and general activation functions. Firstly, for dealing with triple integral term, a new integral inequality is derived. Secondly, based on the theory of convex combination, we propose a novel flexible delay division method and corresponding modified Lyapunov–Krasovskii functional (LKF) is established. Thirdly, a switching estimator design approach is contributed, which ensures that the resulting augmented system is extended dissipative. Combining the extended reciprocally convex technique with Wirtinger-based integral inequality, improved delay-dependent exponential stability criterion is obtained. Finally, a example with two cases is provided to illustrate the feasibility and effectiveness of the developed theoretical results.

Suggested Citation

  • Xie, Wenqian & Zhu, Hong & Zhong, Shouming & Zhang, Dian & Shi, Kaibo & Cheng, Jun, 2018. "Extended dissipative estimator design for uncertain switched delayed neural networks via a novel triple integral inequality," Applied Mathematics and Computation, Elsevier, vol. 335(C), pages 82-102.
    • Handle: RePEc:eee:apmaco:v:335:y:2018:i:c:p:82-102
    DOI: 10.1016/j.amc.2018.04.037
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    References listed on IDEAS

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    1. Ratnavelu, K. & Manikandan, M. & Balasubramaniam, P., 2015. "Synchronization of fuzzy bidirectional associative memory neural networks with various time delays," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 582-605.
    2. Sakthivel, R. & Karthick, S.A. & Kaviarasan, B. & Lim, Yongdo, 2017. "Reliable state estimation of switched neutral system with nonlinear actuator faults via sampled-data control," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 129-147.
    3. Zhang, Ruimei & Zeng, Deqiang & Zhong, Shouming & Yu, Yongbin, 2017. "Event-triggered sampling control for stability and stabilization of memristive neural networks with communication delays," Applied Mathematics and Computation, Elsevier, vol. 310(C), pages 57-74.
    4. Gao, Ming & Sheng, Li & Zhang, Weihai, 2015. "Stochastic H2/H∞ control of nonlinear systems with time-delay and state-dependent noise," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 429-440.
    5. Guo, Runan & Zhang, Ziye & Liu, Xiaoping & Lin, Chong, 2017. "Existence, uniqueness, and exponential stability analysis for complex-valued memristor-based BAM neural networks with time delays," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 100-117.
    6. Wang, Bo & Yan, Juan & Cheng, Jun & Zhong, Shouming, 2017. "New criteria of stability analysis for generalized neural networks subject to time-varying delayed signals," Applied Mathematics and Computation, Elsevier, vol. 314(C), pages 322-333.
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    Cited by:

    1. Cai, Xiao & Zhong, Shouming & Wang, Jun & Shi, Kaibo, 2020. "Robust H∞ control for uncertain delayed T-S fuzzy systems with stochastic packet dropouts," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    2. Qian, Wei & Liu, Haibo & Zhao, Yunji & Li, Yalong, 2022. "Delay-probability-dependent state estimation for neural networks with hybrid delays," Applied Mathematics and Computation, Elsevier, vol. 424(C).

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