Global Asymptotic Stability of Stochastic Fuzzy Cellular Neural Networks with Time-Varying Delays

Wen Guang Luo; Yong Hua Liu; Hong Li Lan

doi:10.4028/www.scientific.net/AMR.139-141.1714

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 139-141Global Asymptotic Stability of Stochastic Fuzzy...

Global Asymptotic Stability of Stochastic Fuzzy Cellular Neural Networks with Time-Varying Delays

Abstract:

In this paper, the problem of global asymptotic stability in the mean square for stochastic fuzzy cellular neural networks (SFCNN) with time-varying delays is investigated. By constructing a newly proposed Lyapunov-Krasovskii function (LKF) and using Ito’s stochastic stability theory, a novel delay-dependent stability criterion is derived. The obtained stability result is helpful to design the stability of fuzzy cellular neural networks (FCNN) with time-varying delays when stochastic noise is taken into consideration. Since it is presented in terms of a linear matrix inequality (LMI), the sufficient condition is easy to be checked efficiently by utilizing some standard numerical packages such as the LMI Control Toolbox in Matlab. Finally, an illustrate example is given to verify the feasibility and usefulness of the proposed result.

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Periodical:

Advanced Materials Research (Volumes 139-141)

Pages:

1714-1717

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.139-141.1714

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Online since:

October 2010

Authors:

Wen Guang Luo, Yong Hua Liu, Hong Li Lan

Keywords:

Global Asymptotic Stability, Linear Matrix Inequality (LMI), Stochastic Fuzzy Cellular Neural Networks, Time-Varying Delay

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References

[1] T. Yang, L. Yang, C.W. Wu and L.O. Chua: Proceedings of the IEEE International Workshop on Cellular Neural Networks and Applications (1996), pp.181-186.

Google Scholar

[2] T. Yang, L. Yang, C.W. Wu and L.O. Chua: Proceedings of the IEEE International Workshop on Cellular Neural Networks and Applications (1996), pp.225-230.

Google Scholar

[3] T. Yang and L. Yang: IEEE Trans. Circuits Syst., Vol. 43 (1996) No. 10, pp.880-883.

Google Scholar

[4] S. Haykin: Neural Networks (Prentice-Hall, New Jersey 1994).

Google Scholar

[5] X. Mao: Stochastic Differential Equation and application (Horwood Publishing, Chicheter 1997).

Google Scholar

[6] R. Rakkiyappan and P. Balasubramaniam: Appl. Math. Comput., Vol. 198 (2008), pp.526-533.

Google Scholar

[7] Y. Guo: Appl. Math. Comput., Vol. 215 (2009), pp.791-795.

Google Scholar

[8] L. Chen and H. Zhao: Neurocomput., Vol. 72 (2008), pp.436-444.

Google Scholar

[9] Z. Liu, H. Zhang and Z. Wang: Neurocomput., Vol. 72 (2009), pp.1056-1064.

Google Scholar

[10] H. Zhang, Z. Wang and D. Liu: IEEE Trans. Circuits Syst., Vol. 54 (2007) No. 8, pp.730-734.

Google Scholar