title: Stability analysis of memristor-based fractional-order neural networks with different memductance functions.
abstract: In this paper, the problem of the existence, uniqueness and uniform stability of memristor-based fractional-order neural networks (MFNNs) with two different types of memductance functions is extensively investigated. Moreover, we formulate the complex-valued memristor-based fractional-order neural networks (CVMFNNs) with two different types of memductance functions and analyze the existence, uniqueness and uniform stability of such networks. By using Banach contraction principle and analysis technique, some sufficient conditions are obtained to ensure the existence, uniqueness and uniform stability of the considered MFNNs and CVMFNNs with two different types of memductance functions. The analysis results establish from the theory of fractional-order differential equations with discontinuous right-hand sides. Finally, four numerical examples are presented to show the effectiveness of our theoretical results.
authors: Department of Mathematics, Bharathiar University, Coimbatore, 641 046 Tamilnadu India.
authors: R
authors: R
authors: Rakkiyappan
authors: Department of Mathematics, Bharathiar University, Coimbatore, 641 046 Tamilnadu India.
authors: G
authors: G
authors: Velmurugan
authors: Department of Mathematics, and Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing, 210096 Jiangsu China ; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, 21589 Saudi Arabia.
authors: Jinde
authors: J
authors: Cao
keywords: Banach contraction principle; Fractional-order; Memristor-based neural networks; Time delays