期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 25, 期 7, 页码 1378-1383出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2013.2285564
关键词
Asymptotic stability; delay-decomposition; linear matrix inequalities (LMIs); neural networks (NNs); time-varying delay
类别
资金
- Science Fund for Distinguished Young Scholars of Hebei Province [F2011203110]
- Hebei Province Hundred Excellent Innovation Talents Support Program
- Ministry of Education of China [20121333110008]
- Hebei Province Applied Basis Research Project [13961806D]
- National Natural Science Foundation of China [60934003, 61290322, 61273222, 61322303]
This brief is concerned with the problem of asymptotic stability of neural networks with time-varying delays. The activation functions are monotone nondecreasing with known lower and upper bounds. Novel stability criteria are derived by employing new Lyapunov-Krasovskii functional and the integral inequality. The developed stability criteria have delay dependencies and the results are characterized by linear matrix inequalities. New and less conservative solutions to the global stability problem are provided in terms of feasibility testing. Numerical examples are finally given to demonstrate the effectiveness of the proposed method.
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