Jun Chen, Fengqiang Ji, Guangming Zhuang, Hongjia Sha
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引用次数: 0
Abstract
This article is concerned with the stability problem for discrete-time neural networks with a time-varying delay. For the two cases that the delay-variation bounds are known and unknown, new augmented Lyapunov–Krasovskii functionals (LKFs) are correspondingly constructed by fully considering the information on the state-related vectors and nonlinear activation function. Through the entire vector-extension method, the forward differences of the new LKFs are estimated to be affine with the delay. Relaxed stability criteria are consequently derived via the convex method. Two numerical examples are provided to show the effectiveness of the proposed method on conservatism reduction.
期刊介绍:
IET Control Theory & Applications is devoted to control systems in the broadest sense, covering new theoretical results and the applications of new and established control methods. Among the topics of interest are system modelling, identification and simulation, the analysis and design of control systems (including computer-aided design), and practical implementation. The scope encompasses technological, economic, physiological (biomedical) and other systems, including man-machine interfaces.
Most of the papers published deal with original work from industrial and government laboratories and universities, but subject reviews and tutorial expositions of current methods are welcomed. Correspondence discussing published papers is also welcomed.
Applications papers need not necessarily involve new theory. Papers which describe new realisations of established methods, or control techniques applied in a novel situation, or practical studies which compare various designs, would be of interest. Of particular value are theoretical papers which discuss the applicability of new work or applications which engender new theoretical applications.