Global exponential stability in Lagrange sense for neutral type recurrent neural networks

Authors:
Qi Luo;Zhigang Zeng;Xiaoxin Liao
Affiliations:
College of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Venue:
Neurocomputing
Year:
2011

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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.