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Delay-Dependent Exponential Stability of Cellular Neural Networks with Multi-Proportional Delays

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Abstract

Global exponential stability of a class of cellular neural networks with multi-proportional delays is investigated. New delay-dependent sufficient conditions ensuring global exponential stability for the system presented here are related to the size of the proportional delay factor, by employing matrix theory and Lyapunov functional, and without assuming the differentiability, boundedness and monotonicity of the activation functions. Two examples and their simulation results are given to illustrate the effectiveness of the obtained results.

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Acknowledgments

The author would like to thank the Editor and the anonymous reviewers for their valuable comments and constructive suggestions. The project is supported by the National Science Foundation of China (No.60974144), Tianjin Municipal Education commission (No.20100813) and Foundation for Doctors of Tianjin Normal University (No.52LX34).

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Authors and Affiliations

  1. Science of Mathematics College, Tianjin Normal University, Tianjin, 300387, China

    Liqun Zhou

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  1. Liqun Zhou
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Correspondence to Liqun Zhou.

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Zhou, L. Delay-Dependent Exponential Stability of Cellular Neural Networks with Multi-Proportional Delays. Neural Process Lett 38, 347–359 (2013). https://doi.org/10.1007/s11063-012-9271-8

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