International Journal of Control, Automation and Systems 2009; 7(2): 281-287
Published online April 6, 2009
https://doi.org/10.1007/s12555-009-0214-8
© The International Journal of Control, Automation, and Systems
This article investigates the problem of robust stability for neural networks with time-varying delays and parameter uncertainties of linear fractional form. By introducing a new Lyapunov-Krasovskii functional and a tighter inequality, delay-dependent stability criteria are established in term of linear matrix inequalities (LMIs). It is shown that the obtained criteria can provide less conservative results than some existing ones. Numerical examples are given to demonstrate the applicability of the proposed approach.
Keywords Delay-dependent, linear matrix inequality, neural networks, robust stability.
International Journal of Control, Automation and Systems 2009; 7(2): 281-287
Published online April 1, 2009 https://doi.org/10.1007/s12555-009-0214-8
Copyright © The International Journal of Control, Automation, and Systems.
Tao Li, Lei Guo, Lingyao Wu, and Changyin Sun
Nanjing University of Information Science and Technology, China
This article investigates the problem of robust stability for neural networks with time-varying delays and parameter uncertainties of linear fractional form. By introducing a new Lyapunov-Krasovskii functional and a tighter inequality, delay-dependent stability criteria are established in term of linear matrix inequalities (LMIs). It is shown that the obtained criteria can provide less conservative results than some existing ones. Numerical examples are given to demonstrate the applicability of the proposed approach.
Keywords: Delay-dependent, linear matrix inequality, neural networks, robust stability.
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