Оригинал (Original)
Автори: Стамов, Т. Г.
Заглавие: Lipschitz stability analysis of fractional-order impulsive delayed reaction-diffusion neural network models
Ключови думи: Lipschitz stability, Reaction-diffusion neural networks, Fra

Абстракт: In this paper, the concept of Lipschitz stability is introduced to impulsive delayed reaction-diffusion neural network models of fractional order. Such networks are an appropriate modeling tool for studying various problems in engineering, biology, neuroscience and medicine. Fractional derivatives of Caputo type are considered in the model. The effects of impulsive perturbations and delays are also under consideration. Lipschitz stability analysis is performed and sufficient conditions for global uniform Lipschitz stability of the model are established. The Lyapunov function approach combined with the comparison principle are employed in the development of the main results. The proposed criteria extend some existing stability results for such models to the Lipschitz stability case. The introduced concept is also very useful in numerous inverse problems.

Библиография

    Издание

    Chaos, Solitons and Fractals, том 162, 2022, Обединеното кралство, Elsevier, ISSN 0960-0779
    		Autors: Stamov, T. G.
    Title: Lipschitz stability analysis of fractional-order impulsive delayed reaction-diffusion neural network models
    Keywords: Lipschitz stability, Reaction-diffusion neural networks, Fractional, Delays, Impulses

    Abstract: In this paper, the concept of Lipschitz stability is introduced to impulsive delayed reaction-diffusion neural network models of fractional order. Such networks are an appropriate modeling tool for studying various problems in engineering, biology, neuroscience and medicine. Fractional derivatives of Caputo type are considered in the model. The effects of impulsive perturbations and delays are also under consideration. Lipschitz stability analysis is performed and sufficient conditions for global uniform Lipschitz stability of the model are established. The Lyapunov function approach combined with the comparison principle are employed in the development of the main results. The proposed criteria extend some existing stability results for such models to the Lipschitz stability case. The introduced concept is also very useful in numerous inverse problems.

    References

      Issue

      Chaos, Solitons and Fractals, vol. 162, 2022, United Kingdom, Elsevier, ISSN 0960-0779

      Цитирания (Citation/s):
      1. Extended Stability and Control Strategies for Impulsive and Fractional Neural Networks: A Review of the Recent Results - 2023 - в издания, индексирани в Scopus или Web of Science
      2. Novel order-dependent passivity conditions of fractional generalized Cohen–Grossberg neural networks with proportional delays - 2023 - в издания, индексирани в Scopus или Web of Science
      3. Stability analysis of fractional reaction-diffusion memristor-based neural networks with neutral delays via Lyapunov functions - 2023 - в издания, индексирани в Scopus или Web of Science
      4. Adaptive quasi-synchronization analysis for Caputo delayed Cohen–Grossberg neural networks - 2023 - в издания, индексирани в Scopus или Web of Science
      5. Event-triggered impulsive control for multi-agent systems with actuation delay and continuous/periodic sampling - 2023 - в издания, индексирани в Scopus или Web of Science
      6. Finite-Time Synchronization of Fractional-Order Fuzzy Time-Varying Coupled Neural Networks Subject to Reaction-Diffusion - 2023 - в издания, индексирани в Scopus или Web of Science
      7. Synchronization of fractional-order delayed neural networks with reaction–diffusion terms: Distributed delayed impulsive control - 2023 - в издания, индексирани в Scopus или Web of Science
      8. Asymptotic Behavior of Delayed Reaction-Diffusion Neural Networks Modeled by Generalized Proportional Caputo Fractional Partial Differential Equations - 2023 - в издания, индексирани в Scopus или Web of Science
      9. Globally asymptotic stability analysis for memristor‐based competitive systems of reaction–diffusion delayed neural networks - 2023 - в издания, индексирани в Scopus или Web of Science
      10. Results on finite time passivity of fractional-order quaternion-valued neural networks with time delay via linear matrix inequalities - 2023 - в издания, индексирани в Scopus или Web of Science
      11. Lipschitz Quasistability of Impulsive Cohen–Grossberg Neural Network Models with Delays and Reaction-Diffusion Terms - 2023 - в издания, индексирани в Scopus или Web of Science

      Вид: публикация в международен форум, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus