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Improved delay-dependent stability conditions for recurrent neural networks with multiple time-varying delays |
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| Authors: | Yonggang Chen Shumin Fei Yongmin Li |
| Affiliation: | 1. School of Mathematical Sciences, Henan Institute of Science and Technology, Xinxiang, 453003, People’s Republic of China 2. Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing, 210096, People’s Republic of China 3. School of Science, Huzhou Teachers College, Huzhou, 313000, People’s Republic of China |
| Abstract: | This paper investigates the global asymptotic stability problem for recurrent neural networks with multiple time-varying delays. Using the free-weighting matrix technique, and incorporating the interconnected information between the upper bounds of multiple time-varying delays, two less conservative delay-dependent asymptotic stability conditions are proposed, which are expressed by linear matrix inequalities, and can be conveniently solved by the existing softwares. Numerical examples show the reduce conservatism of the obtained conditions. |
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