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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29725

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Periodic Solutions of Recurrent Neural Networks with Distributed Delays and Impulses on Time Scales
In this paper, by using the continuation theorem of coincidence degree theory, M-matrix theory and constructing some suitable Lyapunov functions, some sufficient conditions are obtained for the existence and global exponential stability of periodic solutions of recurrent neural networks with distributed delays and impulses on time scales. Without assuming the boundedness of the activation functions gj, hj , these results are less restrictive than those given in the earlier references.
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[1] J. Can, J. Wang, Global asymptotic and robust stability of recurrent neural networks with time delays, IEEE Trans. Circuits Syst. I 52 (5) (2005) 417-425.
[2] H. Huang, J. Cao and J. Wang, Global exponential stability and periodic solutions of recurrent neural networks with delays, Phys. Lett. A 298 (5-6) (2002) 393-404.
[3] Y.Y. Wu, Y.Q. Wu, Stability Analysis for Recurrent Neural Networks with Time-varying Delay, International Journal of Automation and Computing 6 (3) (2009) 223-227.
[4] Y.Y. Wu, T. Li, Y.Q. Wu, Improved Exponential Stability Criteria for Recurrent Neural Networks with Time-varying Discrete and Distributed Delays, International Journal of Automation and Computing 7 (2) (2010) 199-204.
[5] C. Xing, Z. Gui, Global Asymptotic Stability of Recurrent Neural Networks with Time-Varying Delays and Impulses, Fourth International Conference on Natural Computation 2 (2008) 394-398.
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[12] Y. Li, S. Gao, Global Exponential Stability for Impulsive BAM Neural Networks with Distributed Delays on Time Scales, Neural Processing Letters 31 (1) (2010) 65-91.
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