 Bounded real lemmas and exponential H∞ control for memristor-based neural networks with unbounded time-varying delaysXianhe Meng, Xian Zhang and Yantao Wang Mathematics and Computers in Simulation (MATCOM), 2023, vol. 210, issue C, 66-81 Abstract: This paper focuses on developing a bounded real lemma (BRL) and designing a state-feedback controller which guarantees a prescribed H∞ performance level for a class of memristor-based neural networks (MNNs) with unbounded time-varying delays. Firstly, a BRL for MNNs is presented by taking a new approach based on system solutions. This approach requires neither transformation of the model nor construction of Lyapunov–Krasovskii functionals, thereby reducing computational effort and complexity. In addition, the obtained BRL contains only a few simple inequalities, which can be easily solved by using MATLAB. Secondly, the condition for the existence of exponential H∞ controller is given based on the obtained BRL. Finally, two simulation examples demonstrate the validity of the theoretical results. Keywords: Memristor-based neural networks; Unbounded time-varying delays; Bounded real lemma; Exponential H∞ control; Global exponential stability; An approach based on system solutions (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:210:y:2023:i:c:p:66-81 DOI: 10.1016/j.matcom.2023.03.014 Access Statistics for this article Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier |
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