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Robust Dissipativity Analysis of Hopfield-Type Complex-Valued Neural Networks with Time-Varying Delays and Linear Fractional Uncertainties

Author

Listed:
  • Pharunyou Chanthorn

    (Research Center in Mathematics and Applied Mathematics, Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Grienggrai Rajchakit

    (Department of Mathematics, Faculty of Science, Maejo University, Chiang Mai 52290, Thailand)

  • Sriraman Ramalingam

    (Department of Science and Humanities, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Tamil Nadu 600 062, India)

  • Chee Peng Lim

    (Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC 3216, Australia)

  • Raja Ramachandran

    (Ramanujan Centre for Higher Mathematics, Alagappa University, Karaikudi 630 004, India)

Abstract

We study the robust dissipativity issue with respect to the Hopfield-type of complex-valued neural network (HTCVNN) models incorporated with time-varying delays and linear fractional uncertainties. To avoid the computational issues in the complex domain, we divide the original complex-valued system into two real-valued systems. We devise an appropriate Lyapunov-Krasovskii functional (LKF) equipped with general integral terms to facilitate the analysis. By exploiting the multiple integral inequality method, the sufficient conditions for the dissipativity of HTCVNN models are obtained via the linear matrix inequalities (LMIs). The MATLAB software package is used to solve the LMIs effectively. We devise a number of numerical models and their empirical results positively ascertain the obtained results.

Suggested Citation

  • Pharunyou Chanthorn & Grienggrai Rajchakit & Sriraman Ramalingam & Chee Peng Lim & Raja Ramachandran, 2020. "Robust Dissipativity Analysis of Hopfield-Type Complex-Valued Neural Networks with Time-Varying Delays and Linear Fractional Uncertainties," Mathematics, MDPI, vol. 8(4), pages 1-22, April.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:4:p:595-:d:345742
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    References listed on IDEAS

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    1. Dan Liu & Song Zhu & Wenting Chang, 2017. "Mean square exponential input-to-state stability of stochastic memristive complex-valued neural networks with time varying delay," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(9), pages 1966-1977, July.
    2. Subramanian, K. & Muthukumar, P. & Lakshmanan, S., 2018. "State feedback synchronization control of impulsive neural networks with mixed delays and linear fractional uncertainties," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 267-281.
    3. Li, Xiaofei & Ding, Deng, 2017. "Mean square exponential stability of stochastic Hopfield neural networks with mixed delays," Statistics & Probability Letters, Elsevier, vol. 126(C), pages 88-96.
    4. Samidurai, R. & Sriraman, R., 2019. "Robust dissipativity analysis for uncertain neural networks with additive time-varying delays and general activation functions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 201-216.
    5. Sriraman, R. & Cao, Yang & Samidurai, R., 2020. "Global asymptotic stability of stochastic complex-valued neural networks with probabilistic time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 103-118.
    6. 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.
    7. Cao, Yang & Sriraman, R. & Shyamsundarraj, N. & Samidurai, R., 2020. "Robust stability of uncertain stochastic complex-valued neural networks with additive time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 207-220.
    8. M. J. Park & O. M. Kwon & E. J. Cha, 2015. "On Stability Analysis for Generalized Neural Networks with Time-Varying Delays," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-11, October.
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    Cited by:

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    2. Pengfei Guo & Yunong Zhang, 2022. "Tracking Control for Triple-Integrator and Quintuple-Integrator Systems with Single Input Using Zhang Neural Network with Time Delay Caused by Backward Finite-Divided Difference Formulas for Multiple-," Mathematics, MDPI, vol. 10(9), pages 1-27, April.
    3. Tranthi, Janejira & Botmart, Thongchai & Weera, Wajaree & La-inchua, Teerapong & Pinjai, Sirada, 2022. "New results on robust exponential stability of Takagi–Sugeno fuzzy for neutral differential systems with mixed time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 714-738.

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