Global exponential stability in Lagrange sense for neutral type recurrent neural networks |
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| Authors: | Qi LuoAuthor Vitae Zhigang ZengAuthor VitaeXiaoxin LiaoAuthor Vitae |
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| Affiliation: | a College of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China
b Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China |
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| Abstract: | In this paper, the global exponential stability in Lagrange sense for continuous neutral type recurrent neural networks (NRNNs) with multiple time delays is studied. Three different types of activation functions are considered, including general bounded and two types of sigmoid activation functions. By constructing appropriate Lyapunov functions, some easily verifiable criteria for the ultimate boundedness and global exponential attractivity of NRNNs are obtained. These results can be applied to monostable and multistable neural networks as well as chaos control and chaos synchronization. |
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| Keywords: | Recurrent neural networks Lagrange stability Global exponential attractivity Delays |
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