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Exponential Stability of Stochastic Neural Networks with Mixed Time-Delays

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Advances in Neural Networks – ISNN 2011 (ISNN 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6675))

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Abstract

This paper investigates the exponential stability of stochastic neural networks with unbounded discrete delays and infinitely distributed delays. By using Lyapunov functions, the semi-martingale convergence theorem and some inequality techniques, the exponential stability in mean square and almost sure exponential stability are obtained. To overcome the difficulties from unbounded delays, some new techniques are introduced. Some earlier results are improved and generalized. An example is given to illustrate the results.

This research was supported by the fundamental research funds for the central universities under grant No.2010MS130.

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Meng, X., Tian, M., Hu, P., Hu, S. (2011). Exponential Stability of Stochastic Neural Networks with Mixed Time-Delays. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21105-8_24

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  • DOI: https://doi.org/10.1007/978-3-642-21105-8_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21104-1

  • Online ISBN: 978-3-642-21105-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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