Browsing by Author "Niezabitowski, M."

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    Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks
    (Elsevier, 2020) Rajchakit, G.; Chanthorn, P.; Niezabitowski, M.; Raja, R.; Baleanu, Dumitru; Pratap, A.
    This paper analyzes the stability and passivity problems for a class of memristor-based fractional-order competitive neural networks (MBFOCNNs) by using Caputo's fractional derivation. Firstly, impulsive effects are taken well into account and effective analysis techniques are used to reflect the system's practically dynamic behavior. Secondly, by using the Lyapunov technique, some sufficient conditions are obtained by linear matrix inequalities (LMIs) to ensure the stability and passivity of the MBFOCNNs, which can be effectively solved by the LMI computational tool in MATLAB. Finally, two numerical models and their simulation results are given to illustrate the effectiveness of the proposed results. © 2020 Elsevier B.V.
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    Citation - WoS: 196
    Citation - Scopus: 199
    Impulsive Effects on Stability and Passivity Analysis of Memristor-Based Fractional-Order Competitive Neural Networks
    (Elsevier, 2020) Chanthorn, P.; Niezabitowski, M.; Raja, R.; Baleanu, D.; Pratap, A.; Rajchakit, G.
    This paper analyzes the stability and passivity problems for a class of memristor-based fractional-order competitive neural networks (MBFOCNNs) by using Caputo's fractional derivation. Firstly, impulsive effects are taken well into account and effective analysis techniques are used to reflect the system's practically dynamic behavior. Secondly, by using the Lyapunov technique, some sufficient conditions are obtained by linear matrix inequalities (LMIs) to ensure the stability and passivity of the MBFOCNNs, which can be effectively solved by the LMI computational tool in MATLAB. Finally, two numerical models and their simulation results are given to illustrate the effectiveness of the proposed results. (C) 2020 Elsevier B.V. All rights reserved.