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Exponential dissipativity analysis of discrete‐time switched memristive neural networks with actuator saturation via quasi‐time‐dependent control
Author(s) -
Wang Jinling,
Jiang Haijun,
Ma Tianlong,
Hu Cheng,
Alsaedi Ahmed
Publication year - 2018
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4367
Subject(s) - control theory (sociology) , dwell time , discrete time and continuous time , exponential stability , artificial neural network , lyapunov function , dissipative system , observer (physics) , exponential function , controller (irrigation) , mathematics , saturation (graph theory) , computer science , control (management) , physics , nonlinear system , mathematical analysis , medicine , clinical psychology , statistics , quantum mechanics , artificial intelligence , machine learning , combinatorics , agronomy , biology
Summary The dissipativity of discrete‐time switched memristive neural networks with actuator saturation is considered in this paper. By constructing a quasi‐time‐dependent Lyapunov function, sufficient conditions are obtained to guarantee the exponential stability and exponential dissipativity for the closed‐loop system with mode‐dependent average dwell time switching. Furthermore, the exponential H ∞ performance of discrete‐time switched memristive neural networks is also analyzed, while the quasi‐time‐dependent controller and observer gains of the desired exponential dissipative and H ∞ performance can be calculated from linear matrix inequalities. Finally, the effectiveness of theoretical results is illustrated through the numerical examples.

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