Abstract
In this paper, we study the exponential stabilization of oscillating neural networks with time-delay through the process called intermittent control. Some exponential stability criteria for the controlled neural networks are established by Lyapunov function and matrix inequality analysis technique. The present results allow us to estimate legitimately the feasible control region of control parameters. Numerical example is also given to show the effectiveness of our proposed results.
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Recommended by Editorial Board member Seul Jung under the direction of Editor Young Il Lee.
The work described in this paper was partially supported by the Fundamental Research Funds for the Central Universities of China (Project No. CDJXS10182215, CDJZR10 18 55 01) and National Natural Science Foundation of China (Grant No. 60974020, 60972155).
Chao Liu is studying for Ph.D degree for Computer Science and Technology at Chongqing University in China. His current research interest includes impulsive systems, switched systems and neural networks.
Chuandong Li has been a professor at the College of Computer Science, Chongqing University, Chongqing 400030, China, since 2007. His current research interest covers time-delay systems, neural networks, chaos control and synchronization, and impulsive dynamical systems.
Shukai Duan is the associate dean and professor of School of Physical Science and Technology, School of Electronics and Information Engineering, Southwest University, China. His recent research interest covers intelligent information processing, artificial neural network, chaos, nonlinear circuit theory and application, memristor and memristive system.
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Liu, C., Li, C. & Duan, S. Stabilization of oscillating neural networks with time-delay by intermittent control. Int. J. Control Autom. Syst. 9, 1074–1079 (2011). https://doi.org/10.1007/s12555-011-0607-3
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DOI: https://doi.org/10.1007/s12555-011-0607-3