Passivity and passification of memristive recurrent neural networks with multi-proportional delays and impulse

Yuxiao Wang, Yuting Cao, Zhenyuan Guo and Shiping Wen

Applied Mathematics and Computation, 2020, vol. 369, issue C

Abstract: The research direction of this paper is passivity and passification of memristive recurrent neural networks (MRNNs) with multi-proportional delays and impulse. Preparing for passive analysis, the model of MRNNs is transformed into the general recurrent neural networks (RNNs) model through the way of non-smooth analysis. Utilizing the proper Lyapunov–Krasovskii functions constructed in this paper and the common matrix inequalities technique, a novel condition is derived which is sufficient to make sure that system is passive. In addition, it relaxes the condition that the symmetric matrices are all required to be positive definite. The final results are presented by linear matrix inequalities (LMIs) and its verification is easy to be carried out by the LMI toolbox. And there are several numerical examples showing the effectiveness and correctness of the derived criteria.

Keywords: Memristive recurrent neural network; Passivity; Passification; Multi-proportional delay; Impulse (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:369:y:2020:i:c:s0096300319308306

DOI: 10.1016/j.amc.2019.124838

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