$${\varvec{p}}$$ th Moment Exponential Stability of Hybrid Delayed Reaction–Diffusion Cohen–Grossberg Neural Networks

Springer Science and Business Media LLC - Tập 46 - Trang 83-111 - 2016
Weiyuan Zhang1,2,3, Junmin Li2, Chenyang Ding2, Keyi Xing3
1Institute of Nonlinear Science, Xianyang Normal University, Xianyang, People’s Republic of China
2School of Mathematics and Statistics, Xidian University, Xi’an, People’s Republic of China
3The State Key Laboratory for Manufacturing Systems Engineering, and Systems Engineering Institute, Xi’an Jiaotong University, Xi’an, People’s Republic of China

Tóm tắt

In this paper, we propose hybrid reaction–diffusion Cohen–Grossberg neural networks (RDCGNNs) with variable coefficients and mixed time delays. By using the Lyapunov–Krasovkii functional approach, stochastic analysis technique and Hardy inequality, some novel sufficient conditions are derived to ensure the pth moment exponential stability of hybrid RDCGNNs with mixed time delays. The obtained sufficient conditions are relevant to the diffusion terms. The results of this paper are novel and improve some of the previously known results. Finally, two numerical examples are provided to verify the usefulness of the obtained results.

Tài liệu tham khảo

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