Global Robust Exponential Stability of Discrete-Time BAM Neural Networks with Time Varying Delays

Rui Zhang; Jian Liu; Ying Zhang; Chang Tao Wang

doi:10.4028/www.scientific.net/AMR.546-547.772

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 546-547Global Robust Exponential Stability of...

Global Robust Exponential Stability of Discrete-Time BAM Neural Networks with Time Varying Delays

Abstract:

In this paper, the global robust exponential stability is discussed for discrete-time bidirectional associative memory (BAM) neural networks with time varying delays. By the linear matrix inequality (LMI) technique and discrete Lyapunov functional combined with inequality techniques, a new global exponential stability criterion of the equilibrium point is obtained for this system. The proposed result is less restrictive, and easier to check in practice. Remarks are made with other previous works to show the superiority of the obtained results, and the simulation example is used to demonstrate the effectiveness of our result.

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Advanced Materials Research (Volumes 546-547)

Pages:

772-777

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.546-547.772

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Online since:

July 2012

Authors:

Rui Zhang, Jian Liu, Ying Zhang, Chang Tao Wang

Keywords:

BAM Neural Network, Discrete Lyapunov Functional, Discrete-Time System, Global Robust Exponential Stabilit, Linear Matrix Inequality (LMI)

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References

[1] B. Kosko: Adaptive bi-directional associative memories. Applied Optics, Vol. 26(1987), pp.4947-4960.

DOI: 10.1364/ao.26.004947

Google Scholar

[2] B. Kosko: Bi-directional associative memories. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 18(1988), pp.49-60.

DOI: 10.1109/21.87054

Google Scholar

[3] J. Hopfield: Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences of the United States of America, Vol. 81(1984), pp.3088-3092.

DOI: 10.1073/pnas.81.10.3088

Google Scholar

[4] H. G. Zhang, Z. S. Wang: Stability analysis of BAM neural networks with time-varying delays. Progress in Natural Science, Vol. 17(2007), pp.206-211.

DOI: 10.1080/10020070612331343247

Google Scholar

[5] B. Cui, X. Lou: Global asymptotic stability of BAM neural networks with distributed delays and reaction–diffusion terms. Chaos, Solitons & Fractals, Vol. 27(2006), pp.1347-1354.

DOI: 10.1016/j.chaos.2005.04.112

Google Scholar

[6] L. Sheng, H.Z. Yang: Novel global robust exponential stability criterion for uncertain BAM neural networks with time-varying delays. Chaos, Solitons & Fractals, Vol. 40(2009), pp.2102-2113.

DOI: 10.1016/j.chaos.2007.09.098

Google Scholar

[7] X. Liu, M. Tang, R. Martin, X. Liu: Discrete-time BAM neural networks with variable delays. Phys. Lett. A, Vol. 367(2007), pp.322-330.

DOI: 10.1016/j.physleta.2007.03.037

Google Scholar

[8] Z.Q. Zhang, D.M. Zhou: Existence and global exponential stability of a periodic solution for a discrete-time interval general BAM neural networks. Journal of the Franklin Institute, Vol. 347(2010), pp.763-780.

DOI: 10.1016/j.jfranklin.2010.02.007

Google Scholar

[9] S.Y. Xu, J. Lam, D.W.C. Ho: Novel global robust stability criteria for interval neural networks with multiple time-varying delays. Physics Letters A, Vol. 342(2005), pp.322-330.

DOI: 10.1016/j.physleta.2005.05.016

Google Scholar

[10] J. Cao, D.W.C. Ho, X. Huang: LMI-based criteria for global robust stability of bidirectional associative memory networks with time delay. Nonlinear Analysis, Vol. 66(2007), pp.1558-1572.

DOI: 10.1016/j.na.2006.02.009

Google Scholar

[11] X. Lou, B. Cui: On the global robust asymptotic stability of BAM neural networks with time-varying delays. Neurocomputing, Vol. 70(2006), pp.273-279.

DOI: 10.1016/j.neucom.2006.02.020

Google Scholar

[12] M. Gao, B.T. Cui: Global robust exponential stability of discrete-time interval BAM neural networks with time-varying delays. Applied Mathematical Modelling, Vol33(2009), pp.1270-1284.

DOI: 10.1016/j.apm.2008.01.019

Google Scholar

[13] R. Zhang, C.G. Wang: Global robust exponential stability of Cohen-Grossberg neural network with time varying delays. American Journal of Engineering and Technology Research, vol. 11(2011), pp.500-504.

DOI: 10.4028/www.scientific.net/amm.182-183.1135

Google Scholar

[14] R. Zhang, Z.S. Wang, J. Feng, Y.W. Jing: Delay-dependent exponential stability of discrete-time BAM neural networks with time varying delays. ISNN 2009-6th International Symposium on Neural Networks(2009), pp.440-449.

DOI: 10.1007/978-3-642-01507-6_51

Google Scholar