Mean Square Exponential Stability of Stochastic High-Order Hopfield Neural Networks with Time-Varying Delays

Abstract:

Article Preview

The paper considers the problems of global exponential stability for stochastic delayed high-order Hopfield neural networks with time-varying delays. By employing the linear matrix inequality(LMI) and the Lyapunov functional methods, we present some new criteria ensuring globally mean square exponential stability. The results impose constraint conditions on the network parameters of neural system independent. The results are applicable to all continuous non-monotonic neuron activation functions.

Info:

Periodical:

Edited by:

Yun-Hae Kim and Prasad Yarlagadda

Pages:

1532-1535

Citation:

X. D. Shi, "Mean Square Exponential Stability of Stochastic High-Order Hopfield Neural Networks with Time-Varying Delays", Applied Mechanics and Materials, Vols. 303-306, pp. 1532-1535, 2013

Online since:

February 2013

Authors:

Export:

Price:

$38.00

[1] J. Hopfield. Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Natl. Acad. Sci. U.S.A. Biophysics 81 (1984) 3088-3092.

DOI: https://doi.org/10.1073/pnas.81.10.3088

[2] L. O. Chua, L. Yang. Cellular networks: theory. IEEE Trans. Syst. 35 (10) (1988) 1257- 1272.

[3] L. Personnaz, I. Guyon, G. Dreyfus. High-order neural networks: information storage without errors. Euro Phys. Lett. 4 (8) (1987) 863-867.

DOI: https://doi.org/10.1209/0295-5075/4/8/001

[4] D. Psaltis, C. H. Par, J. Hong. Higher-order associative memories and their optical implementations. Neural Networks 1 (2) (1988) 149-163.

DOI: https://doi.org/10.1016/0893-6080(88)90017-2

[5] X.X. Liao, X. Mao, Exponential stability and instability of stochastic neural networks, Stochastic. Anal. Appl. 14(2) (1996a)165-185.

[6] Lin Wan, Jianhua Sun. Mean square wxponential stability of stochastic delayed Hopfield neural networks. Physics Letters A 343 (2005) 306-318.

DOI: https://doi.org/10.1016/j.physleta.2005.06.024