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Dynamic stability analysis of stochastic fractional-order memristor fuzzy BAM neural networks with delay and leakage terms

Author

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  • Syed Ali, M.
  • Narayanan, Govindasamy
  • Shekher, Vineet
  • Alsulami, Hamed
  • Saeed, Tareq

Abstract

This paper deals with the uniform stability in mean square of stochastic fractional-order memristor fuzzy BAM neural networks with delay and leakage terms. By employing the ideas of Cauchy–Schwartz inequality, Burkholder–Davis–Gundy inequality, analysis technique, some sufficient conditions are derived to ensure the uniform stability in mean square of such networks. The existence, uniqueness and stability of its equilibrium point are also showed. Two different fractional-order derivatives between the U-layer and V-layer are taken into consideration synchronously with fractional order 1/2 ≤ α < 1. Two examples are also provided to illustrate the effectiveness of our results.

Suggested Citation

  • Syed Ali, M. & Narayanan, Govindasamy & Shekher, Vineet & Alsulami, Hamed & Saeed, Tareq, 2020. "Dynamic stability analysis of stochastic fractional-order memristor fuzzy BAM neural networks with delay and leakage terms," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    • Handle: RePEc:eee:apmaco:v:369:y:2020:i:c:s0096300319308884
    DOI: 10.1016/j.amc.2019.124896
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    References listed on IDEAS

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    1. Yuan, Manman & Wang, Weiping & Luo, Xiong & Liu, Linlin & Zhao, Wenbing, 2018. "Finite-time anti-synchronization of memristive stochastic BAM neural networks with probabilistic time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 244-260.
    2. Zhang, Guodong & Zeng, Zhigang, 2018. "Exponential stability for a class of memristive neural networks with mixed time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 544-554.
    3. Huang, Chengdai & Meng, Yijie & Cao, Jinde & Alsaedi, Ahmed & Alsaadi, Fuad E., 2017. "New bifurcation results for fractional BAM neural network with leakage delay," Chaos, Solitons & Fractals, Elsevier, vol. 100(C), pages 31-44.
    4. Zhang, Weiwei & Cao, Jinde & Wu, Ranchao & Chen, Dingyuan & Alsaadi, Fuad E., 2018. "Novel results on projective synchronization of fractional-order neural networks with multiple time delays," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 76-83.
    5. Yang, Xujun & Li, Chuandong & Huang, Tingwen & Song, Qiankun, 2017. "Mittag–Leffler stability analysis of nonlinear fractional-order systems with impulses," Applied Mathematics and Computation, Elsevier, vol. 293(C), pages 416-422.
    6. Liu, Shuxin & Yu, Yongguang & Zhang, Shuo & Zhang, Yuting, 2018. "Robust stability of fractional-order memristor-based Hopfield neural networks with parameter disturbances," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 845-854.
    7. Zhang, Lei & Song, Qiankun & Zhao, Zhenjiang, 2017. "Stability analysis of fractional-order complex-valued neural networks with both leakage and discrete delays," Applied Mathematics and Computation, Elsevier, vol. 298(C), pages 296-309.
    8. Yuping Cao & Chuanzhi Bai, 2014. "Finite-Time Stability of Fractional-Order BAM Neural Networks with Distributed Delay," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-8, April.
    9. Ullah, Saif & Altaf Khan, Muhammad & Farooq, Muhammad, 2018. "A fractional model for the dynamics of TB virus," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 63-71.
    10. Wang, Weiping & Yu, Minghui & Luo, Xiong & Liu, Linlin & Yuan, Manman & Zhao, Wenbing, 2017. "Synchronization of memristive BAM neural networks with leakage delay and additive time-varying delay components via sampled-data control," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 84-97.
    11. 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.
    12. Syed Ali, M. & Balasubramaniam, P., 2009. "Global exponential stability of uncertain fuzzy BAM neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 42(4), pages 2191-2199.
    13. Zhang, Jianmei & Wu, Jianwei & Bao, Haibo & Cao, Jinde, 2018. "Synchronization analysis of fractional-order three-neuron BAM neural networks with multiple time delays," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 441-450.
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    Cited by:

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    2. Xu, Changjin & Liu, Zixin & Liao, Maoxin & Li, Peiluan & Xiao, Qimei & Yuan, Shuai, 2021. "Fractional-order bidirectional associate memory (BAM) neural networks with multiple delays: The case of Hopf bifurcation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 182(C), pages 471-494.
    3. Syed Ali, M. & Narayanan, G. & Saroha, Sumit & Priya, Bandana & Thakur, Ganesh Kumar, 2021. "Finite-time stability analysis of fractional-order memristive fuzzy cellular neural networks with time delay and leakage term," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 468-485.
    4. Zhang, Guodong & Cao, Jinde, 2023. "New results on fixed/predefined-time synchronization of delayed fuzzy inertial discontinuous neural networks: Non-reduced order approach," Applied Mathematics and Computation, Elsevier, vol. 440(C).
    5. Xu, Changjin & Liu, Zixin & Yao, Lingyun & Aouiti, Chaouki, 2021. "Further exploration on bifurcation of fractional-order six-neuron bi-directional associative memory neural networks with multi-delays," Applied Mathematics and Computation, Elsevier, vol. 410(C).
    6. Yang, Zhanying & Zhang, Jie & Zhang, Zhihui & Mei, Jun, 2023. "An improved criterion on finite-time stability for fractional-order fuzzy cellular neural networks involving leakage and discrete delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 910-925.
    7. Narayanan, G. & Syed Ali, M. & Karthikeyan, Rajagopal & Rajchakit, Grienggrai & Jirawattanapanit, Anuwat, 2022. "Novel adaptive strategies for synchronization control mechanism in nonlinear dynamic fuzzy modeling of fractional-order genetic regulatory networks," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    8. Li, Hong-Li & Kao, Yonggui & Hu, Cheng & Jiang, Haijun & Jiang, Yao-Lin, 2021. "Robust exponential stability of fractional-order coupled quaternion-valued neural networks with parametric uncertainties and impulsive effects," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).

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