Global exponential stability in Lagrange sense for recurrent neural networks with time delays |
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| Authors: | Xiaoxin Liao Qi Luo Zhigang Zeng Yunxia Guo |
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| Institution: | aDepartment of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China;bDepartment of Information and Communication, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China;cSchool of Automation, Wuhan University of Technology, Wuhan, Hubei 430070, China;dZhuhai Radio and TV University, Zhuhai, Guangdong 51900, China |
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| Abstract: | In this paper, we study the global exponential stability in Lagrange sense for continuous recurrent neural networks (RNNs) with multiple time delays. Three different types of activation functions are considered, which include both bounded and unbounded activation functions. By constructing appropriate Lyapunov-like functions, we provide easily verifiable criteria for the boundedness and global exponential attractivity of RNNs. These results can be applied to analyze monostable as well as multistable neural networks. |
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| Keywords: | Recurrent neural networks Lagrange stability Global exponential attractivity Delays |
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