Extended robust global exponential stability for uncertain switched memristor-based neural networks with time-varying delays
作者： Xiaoqing Li , Kun She , Shouming Zhong , Kaibo Shi , Wei Kang
DOI: 10.1016/J.AMC.2017.12.032
关键词:
摘要: Abstract This paper is concerned with the problem of global exponential stability for uncertain memristive-based neural networks (UMNNs) time-varying delays and switching parameters subject to unstable subsystems. Different from most existing papers, considered switched MNNs discrete-delays are modeled as (SNNs) parameters. Based on multiple Lyapunov–Krasovskii functional (MLF) approach, average dwell time (ADT) technique mode-dependent (MDADT) method, some LMIs-based criteria derived design signal guarantee networks. By exploring property each subsystem, all subsystems categorized into stable ones. The SNNs both more general applicable than models only view being stable, thus getting less conservatism criteria. proposed sufficient conditions can be simplified forms LMIs conveniently using Matlab LMI toolbox. Finally, two numerical examples exploited demonstrate effectiveness applicability theoretical results.
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