International Journal of Control, Automation and Systems 2017; 15(4): 1888-1900
Published online July 10, 2017
https://doi.org/10.1007/s12555-016-9483-1
© The International Journal of Control, Automation, and Systems
The objective of this paper is to analyze the stability analysis of neutral-type neural networks with additive time-varying delay and leakage delay. By constructing a suitable augmented Lyapunov-Krasovskii functional with triple and four integral terms, some new stability criteria are established in terms of linear matrix inequalities, which is easily solved by various convex optimization techniques. More information of the lower and upper delay bounds of time-varying delays are used to derive the stability criteria, which can lead less conservative results. The obtained conditions are expressed with linear matrix inequalities (LMIs) whose feasible can be checked easily by MATLAB LMI control toolbox. Finally, two numerical examples are given to demonstrate the effectiveness of the proposed method."
Keywords Additive time-varying delays, linear matrix inequality, Lyapunov-Krasovskii functional, neural networks, neutral-type.
International Journal of Control, Automation and Systems 2017; 15(4): 1888-1900
Published online August 1, 2017 https://doi.org/10.1007/s12555-016-9483-1
Copyright © The International Journal of Control, Automation, and Systems.
R. Samidurai, S. Rajavel, R. Sriraman, Jinde Cao*, Ahmed Alsaedi and Fuad E. Alsaadi
Southeast University
The objective of this paper is to analyze the stability analysis of neutral-type neural networks with additive time-varying delay and leakage delay. By constructing a suitable augmented Lyapunov-Krasovskii functional with triple and four integral terms, some new stability criteria are established in terms of linear matrix inequalities, which is easily solved by various convex optimization techniques. More information of the lower and upper delay bounds of time-varying delays are used to derive the stability criteria, which can lead less conservative results. The obtained conditions are expressed with linear matrix inequalities (LMIs) whose feasible can be checked easily by MATLAB LMI control toolbox. Finally, two numerical examples are given to demonstrate the effectiveness of the proposed method."
Keywords: Additive time-varying delays, linear matrix inequality, Lyapunov-Krasovskii functional, neural networks, neutral-type.
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