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Global Mean Square Exponential Stability of Impulsive Non-autonomous Stochastic Neural Networks with Mixed Delays

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Abstract

In this work, we consider a class of impulsive non-autonomous stochastic neural networks with mixed delays. By establishing a new generalized Halanay inequality with impulses, we obtain some sufficient conditions ensuring global mean square exponential stability of the addressed neural networks. The sufficient conditions are easily checked in practice by simple algebra methods and have a wider adaptive range. An example is given to illustrate our results.

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Acknowledgments

The work is supported by National Natural Science Foundation of China under Grants 11271270, 11326118, 11501065 and 11201320, Fundamental Research Funds for the Central Universities under Grant 2682015CX059 and Natural Science Foundation of Chongqing under Grant cstc2015jcyjA00033.

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Authors and Affiliations

  1. School of Mathematics, Southwest Jiaotong University, Chengdu, 610031, China

    Dingshi Li

  2. College of Science, Chongqing Jiaotong University, Chongqing, 400074, China

    Bing Li

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  1. Dingshi Li
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  2. Bing Li
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Correspondence to Dingshi Li.

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Li, D., Li, B. Global Mean Square Exponential Stability of Impulsive Non-autonomous Stochastic Neural Networks with Mixed Delays. Neural Process Lett 44, 751–764 (2016). https://doi.org/10.1007/s11063-015-9492-8

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  • DOI: https://doi.org/10.1007/s11063-015-9492-8

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