Bilgilendirme: Sürüm Güncellemesi ve versiyon yükseltmesi nedeniyle, geçici süreyle zaman zaman kesintiler yaşanabilir ve veri içeriğinde değişkenlikler gözlemlenebilir. Göstereceğiniz anlayış için teşekkür ederiz.

Browsing by Author "Niezabitowski, M."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Article
    Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks
    (2020) Rajchakit, G.; Chanthorn, P.; Niezabitowski, M.; Raja, R.; Baleanu, Dumitru; Pratap, A.; 56389; 02.02. Matematik; 02. Fen-Edebiyat Fakültesi; 01. Çankaya Üniversitesi
    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.
  • Thumbnail Image
    Article
    Citation - WoS: 189
    Citation - Scopus: 194
    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.; 56389; 02.02. Matematik; 02. Fen-Edebiyat Fakültesi; 01. Çankaya Üniversitesi
    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.