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LMI-based results on exponential stability of BAM-type neural networks with leakage and both time-varying delays: A non-fragile state estimation approach

Author

Listed:
  • Maharajan, C.
  • Raja, R.
  • Cao, Jinde
  • Rajchakit, G.
  • Tu, Zhengwen
  • Alsaedi, Ahmed

Abstract

In this epigrammatic, the problem of exponential stability for BAM-type neural networks (BAMNNs) with non-fragile state estimator is investigated under time-varying delays. The delays in discrete and distributed terms are assumed to be time-varying, which means that the lower and upper bounds can be derived. Without involving the time-delays or the activation functions, the non-fragile estimators are constructed in terms of simple linear formation and also the implementation of state estimators are uncomplicated. In addition, the non-fragile estimators are reduced the possible implementation errors in neural networks. For consequence, reason of energy saving, the non-fragile estimators are designed with neural networks. By fabricating a suitable LKF (Lyapunov–Krasovskii functional) and enroling some analysis techniques, a novel sufficient conditions for exponential stability of the designated neural networks are derived in terms of Linear Matrix Inequalities (LMIs), which can be easily assessed by MATLAB LMI Control toolbox. Accordingly, the research proposed here, is advanced and less conservative than the previous one exists in the literature. Finally, two numerical examples with simulations and comparative studies are performed to substantiate the advantage and validity of our theoretical findings.

Suggested Citation

  • Maharajan, C. & Raja, R. & Cao, Jinde & Rajchakit, G. & Tu, Zhengwen & Alsaedi, Ahmed, 2018. "LMI-based results on exponential stability of BAM-type neural networks with leakage and both time-varying delays: A non-fragile state estimation approach," Applied Mathematics and Computation, Elsevier, vol. 326(C), pages 33-55.
    • Handle: RePEc:eee:apmaco:v:326:y:2018:i:c:p:33-55
    DOI: 10.1016/j.amc.2018.01.001
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    References listed on IDEAS

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    1. Li, Ruoxia & Cao, Jinde, 2016. "Stability analysis of reaction-diffusion uncertain memristive neural networks with time-varying delays and leakage term," Applied Mathematics and Computation, Elsevier, vol. 278(C), pages 54-69.
    2. Raja, R. & Zhu, Quanxin & Senthilraj, S. & Samidurai, R., 2015. "Improved stability analysis of uncertain neutral type neural networks with leakage delays and impulsive effects," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 1050-1069.
    3. R. Saravanakumar & M. Syed Ali & Jinde Cao & He Huang, 2016. "state estimation of generalised neural networks with interval time-varying delays," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(16), pages 3888-3899, December.
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    Cited by:

    1. Manickam Iswarya & Ramachandran Raja & Grienggrai Rajchakit & Jinde Cao & Jehad Alzabut & Chuangxia Huang, 2019. "Existence, Uniqueness and Exponential Stability of Periodic Solution for Discrete-Time Delayed BAM Neural Networks Based on Coincidence Degree Theory and Graph Theoretic Method," Mathematics, MDPI, vol. 7(11), pages 1-18, November.
    2. Cao, Yang & Sriraman, R. & Samidurai, R., 2020. "Stability and stabilization analysis of nonlinear time-delay systems with randomly occurring controller gain fluctuation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 36-51.
    3. Balasundaram, K. & Raja, R. & Pratap, A. & Chandrasekaran, S., 2019. "Impulsive effects on competitive neural networks with mixed delays: Existence and exponential stability analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 290-302.
    4. Wang, Chen & Zhang, Hai & Ye, Renyu & Zhang, Weiwei & Zhang, Hongmei, 2023. "Finite time passivity analysis for Caputo fractional BAM reaction–diffusion delayed neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 424-443.
    5. Oliveira, José J., 2022. "Global stability criteria for nonlinear differential systems with infinite delay and applications to BAM neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    6. Chen, Dazhao & Zhang, Zhengqiu, 2022. "Finite-time synchronization for delayed BAM neural networks by the approach of the same structural functions," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    7. Xu, Changjin & Liao, Maoxin & Li, Peiluan & Yuan, Shuai, 2021. "Impact of leakage delay on bifurcation in fractional-order complex-valued neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    8. Dianavinnarasi, J. & Raja, R. & Alzabut, J. & Cao, J. & Niezabitowski, M. & Bagdasar, O., 2022. "Application of Caputo–Fabrizio operator to suppress the Aedes Aegypti mosquitoes via Wolbachia: An LMI approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 462-485.

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