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Guaranteed Cost Stabilization of Cellular Neural Networks with Time‐Varying Delay
Author(s) -
He Hanlin,
Yan Lu,
Tu Jianjun
Publication year - 2013
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.631
Subject(s) - cellular neural network , control theory (sociology) , exponential stability , quadratic equation , artificial neural network , stability (learning theory) , computer science , linear matrix inequality , matrix (chemical analysis) , control (management) , mathematics , mathematical optimization , artificial intelligence , nonlinear system , machine learning , physics , geometry , materials science , quantum mechanics , composite material
Guaranteed cost stabilization of cellular neural networks with time‐varying delay ( DCNNs ) is considered in this paper. Via applying the zoned discussion and maximum synthesis ( ZDMS ) in DCNNs and Lyapunov–Krasovskii functional, a less conservative feedback control law in the form of quadratic matrix inequality ( QMI ) is derived to achieve globally asymptotic stability of the system.