Finite-time dissipative control for discrete-time memristive neural networks via interval matrix method

Jinrong Yang, Guici Chen, Shiping Wen and Leimin Wang

Chaos, Solitons & Fractals, 2023, vol. 176, issue C

Abstract: This paper addresses the problems of finite-time dissipative analysis and control for discrete-time memristive neural networks (DMNNs). With the help of interval matrix method (IMM), the challenges posed by the mismatched state-dependent parameters of DMNNs can be solved, which is different from the maximal absolute value operation-based method (MAVOM) in most existing literature. Based on a discrete-time Lyapunov-Krasovskii functional (LKF) and some inequality techniques, several sufficient conditions are established for achieving both finite-time bounded (FTB) behavior and finite-time (Q,S,R)−γ dissipative (FTD). Moreover, the control gains are obtained by solving a series of linear matrix inequalities (LMIs) and convex optimization problems. Finally, the validity of our main findings and the superiority of the control strategies are verified through numerical simulations.

Keywords: Finite-time dissipative (FTD); Interval matrix method (IMM); Discrete-time memristive neural networks (DMNNs) (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:176:y:2023:i:c:s0960077923010639

DOI: 10.1016/j.chaos.2023.114161

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