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Attractor and Stochastic Boundedness for Stochastic Infinite Delay Neural Networks with Markovian Switching

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Abstract

First, we establish the stochastic LaSalle theorem for stochastic infinite delay differential equations with Markovian switching, from which some criterias on attraction are obtained. Then, by employing Lyapunov method and LaSalle-type theorem established above, we obtain some sufficient conditions ensuring the attractor and stochastic boundedness for stochastic infinite delay neural networks with Markovian switching. Finally, an example is also discussed to illustrate the efficiency of the obtained results.

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Acknowledgments

The work is supported by National Natural Science Foundation of China under Grant 10971147 and Fundamental Research Funds for the Central Universities 2010SCU1006.

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Authors and Affiliations

  1. School of Mathematics, Southwest Jiaotong University, Chengdu, 610031, People’s Republic of China

    Dingshi Li & Chao Ma

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  1. Dingshi Li
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  2. Chao Ma
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Correspondence to Dingshi Li.

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Li, D., Ma, C. Attractor and Stochastic Boundedness for Stochastic Infinite Delay Neural Networks with Markovian Switching. Neural Process Lett 40, 127–142 (2014). https://doi.org/10.1007/s11063-013-9314-9

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