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Robust dissipativity analysis for uncertain neural networks with additive time-varying delays and general activation functions

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  • Samidurai, R.
  • Sriraman, R.

Abstract

This paper deals with the problem of delay-dependent robust dissipativity analysis for uncertain neural networks with additive time varying delays by using a more general activation function approach. Different from previous literature, some sufficient information on neuron activation function and additive time-varying delays have been considered. By constructing suitable Lyapunov–Krasovskii functionals (LKFs) with some new integral terms, and estimating their derivative by using newly developed single integral inequality that includes Jensen’s inequality and Wirtinger-based integral inequality as a special case. A new delay-dependent less conservative global asymptotic stability and dissipative criteria have been established in the form of linear matrix inequalities (LMIs) technique. The effectiveness and advantages of the proposed results are verified by available standard numerical packages.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:matcom:v:155:y:2019:i:c:p:201-216
    DOI: 10.1016/j.matcom.2018.03.010
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    References listed on IDEAS

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    1. Song, Qiankun & Cao, Jinde, 2007. "Global robust stability of interval neural networks with multiple time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 74(1), pages 38-46.
    2. Rajavel, S. & Samidurai, R. & Cao, Jinde & Alsaedi, Ahmed & Ahmad, Bashir, 2017. "Finite-time non-fragile passivity control for neural networks with time-varying delay," Applied Mathematics and Computation, Elsevier, vol. 297(C), pages 145-158.
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    Cited by:

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    2. Sakthivel, R. & Suveetha, V.T. & Nithya, V. & Sakthivel, R., 2021. "Finite-time reliable filtering for Takagi–Sugeno fuzzy semi-Markovian jump systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 403-418.
    3. Usa Humphries & Grienggrai Rajchakit & Pramet Kaewmesri & Pharunyou Chanthorn & Ramalingam Sriraman & Rajendran Samidurai & Chee Peng Lim, 2020. "Global Stability Analysis of Fractional-Order Quaternion-Valued Bidirectional Associative Memory Neural Networks," Mathematics, MDPI, vol. 8(5), pages 1-27, May.
    4. Li, Liangchen & Xu, Rui & Lin, Jiazhe, 2020. "Lagrange stability for uncertain memristive neural networks with Lévy noise and leakage delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    5. Harshavarthini, S. & Sakthivel, R. & Ma, Yong-Ki & Muslim, M., 2020. "Finite-time resilient fault-tolerant investment policy scheme for chaotic nonlinear finance system," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    6. 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.
    7. Wang, Limin & Song, Qiankun, 2020. "Pricing policies for dual-channel supply chain with green investment and sales effort under uncertain demand," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 79-93.

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