Novel delay-distribution-dependent stability analysis for continuous-time recurrent neural networks with stochastic delay

doi:10.1088/1674-1056/21/12/120701

Abstract In this paper, the problem of delay-distribution-dependent stability is investigated for continuous-time recurrent neural networks (CRNNs) with stochastic delay. Different from the common assumptions on time delays, it is assumed that the probability distribution of the delay taking values in some intervals is known a priori. By making full use of the information concerning the probability distribution of the delay and by using a tighter bounding technique (reciprocally convex combination method), less conservative asymptotic mean-square stable sufficient conditions are derived in terms of linear matrix inequalities (LMIs). Two numerical examples show that our results are better than the existing ones.

Keywords: recurrent neural networks stochastic delay mean-square stability linear matrix inequality

Received: 11 May 2012
Revised: 14 June 2012
Accepted manuscript online:

PACS:	07.05.Mh	(Neural networks, fuzzy logic, artificial intelligence)
	02.10.Yn	(Matrix theory)
	02.30.Yy	(Control theory)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61273164, 61034005, and 60974071), the National High Technology Research and Development Program of China (Grant No. 2012AA040104), and the Fundamental Research Funds for the Central Universities (Grant Nos. N100104102 and N110604007).

Corresponding Authors: Feng Jian
E-mail: fjneu@163.com

Cite this article:

Wang Shen-Quan (王申全), Feng Jian (冯健), Zhao Qing (赵青) Novel delay-distribution-dependent stability analysis for continuous-time recurrent neural networks with stochastic delay 2012 Chin. Phys. B 21 120701

[1]	Zhang H G and Quan Y B 2001 IEEE Trans. Fuzzy Syst. 9 349
[2]	Ozcan N and Arik S 2006 IEEE Trans. Circ. Syst. 53 166
[3]	Zuo Z Q, Yang C L and Wang Y J 2010 IEEE Trans. Neural Netw. 21 339
[4]	Li T, Guo L, Sun C Y and Lin C 2008 IEEE Trans. Neural Netw. 19 726
[5]	He Y, Liu G P, Rees D and Wu M 2007 IEEE Trans. Neural Netw. 18 1850
[6]	Kwon O M, Kwon J W and Kim S H 2011 Chin. Phys. B 20 050505
[7]	Zhang X M and Han Q L 2011 IEEE Trans. Neural Netw. 22 1180
[8]	Zhang H G, Liu Z W, Huang G B and Wang Z S 2010 IEEE Trans. Neural Netw. 21 91
[9]	Qiu F, Cui B T and Ji Y 2009 Chin. Phys. B 18 5203
[10]	Wang J A 2011 Chin. Phys. B 20 120701
[11]	Lee S M, Kwon O M and Park J H 2010 Chin. Phys. B 19 050507
[12]	Lakshmanan S and Balasubramaniam P 2011 Chin. Phys. B 20 040204
[13]	Wu Z G, Lam J, Su H Y and Chu J 2012 IEEE Trans. Neural Netw. Learning Syst. 23 199
[14]	Shao H Y 2008 IEEE Trans. Neural Netw. 19 1647
[15]	Cao J D and Wang J 2005 IEEE Trans. Circuits Syst. 52 417
[16]	Zhang H G, Wang Z S and Liu D R 2008 IEEE Trans. Neural Netw. 19 855
[17]	Syed Ali M 2012 Chin. Phys. B 21 070207
[18]	Zhao Y, Gao H J, Lam J and Chen K 2009 IEEE Trans. Neural Netw. 20 1330
[19]	Yue D, Zhang Y J, Tian E G and Peng C 2008 IEEE Trans. Neural Netw. 19 1299
[20]	Wu M, Liu F, Shi P, He Y and Yokoyama R 2008 IEEE Trans. Syst. Man Cybern. B 38 1152
[21]	Mou S S, Gao H J, Qiang W Y and Chen K 2008 IEEE Trans. Syst. Man Cybern. B 38 571
[22]	Park P G, Ko J W and Jeong C 2011 Automatica 47 235

[1]	Adaptive synchronization of chaotic systems with less measurement and actuation Shun-Jie Li(李顺杰), Ya-Wen Wu(吴雅文), and Gang Zheng(郑刚). Chin. Phys. B, 2021, 30(10): 100503.
[2]	Robust H_∞ control for uncertain Markovian jump systems with mixed delays R Saravanakumar, M Syed Ali. Chin. Phys. B, 2016, 25(7): 070201.
[3]	Robust H_∞ control of uncertain systems with two additive time-varying delays M. Syed Ali, R. Saravanakumar. Chin. Phys. B, 2015, 24(9): 090202.
[4]	Stability analysis of Markovian jumping stochastic Cohen–Grossberg neural networks with discrete and distributed time varying delays M. Syed Ali. Chin. Phys. B, 2014, 23(6): 060702.
[5]	Improved delay-dependent robust H_∞ control of an uncertain stochastic system with interval time-varying and distributed delays M. Syed Ali, R. Saravanakumar. Chin. Phys. B, 2014, 23(12): 120201.
[6]	Cluster exponential synchronization of a class of complex networks with hybrid coupling and time-varying delay Wang Jun-Yi (王军义), Zhang Hua-Guang (张化光), Wang Zhan-Shan (王占山), Liang Hong-Jing (梁洪晶). Chin. Phys. B, 2013, 22(9): 090504.
[7]	Novel delay dependent stability analysis of Takagi–Sugeno fuzzy uncertain neural networks with time varying delays M. Syed Ali . Chin. Phys. B, 2012, 21(7): 070207.
[8]	Robust H_∞ control for uncertain systems with heterogeneous time-varying delays via static output feedback Wang Jun-Wei (王军威), Zeng Cai-Bin (曾才斌 ). Chin. Phys. B, 2012, 21(11): 110206.
[9]	Novel stability criteria for fuzzy Hopfield neural networks based on an improved homogeneous matrix polynomials technique Feng Yi-Fu (冯毅夫), Zhang Qing-Ling (张庆灵), Feng De-Zhi (冯德志). Chin. Phys. B, 2012, 21(10): 100701.
[10]	Robust stability analysis of Takagi–Sugeno uncertain stochastic fuzzy recurrent neural networks with mixed time-varying delays M. Syed Ali . Chin. Phys. B, 2011, 20(8): 080201.
[11]	A novel mixed-synchronization phenomenon in coupled Chua's circuits via non-fragile linear control Wang Jun-Wei(王军威), Ma Qing-Hua(马庆华), and Zeng Li(曾丽) . Chin. Phys. B, 2011, 20(8): 080506.
[12]	The $\mathscr{H}_{\infty}$ synchronization of nonlinear Bloch systems via dynamic feedback control approach D.H. Ji, J.H. Koo, S.C. Won, and Ju H. Park. Chin. Phys. B, 2011, 20(7): 070502.
[13]	Linear matrix inequality approach for robust stability analysis for stochastic neural networks with time-varying delay S. Lakshmanan and P. Balasubramaniam. Chin. Phys. B, 2011, 20(4): 040204.
[14]	New synchronization analysis for complex networks with variable delays Zhang Hua-Guang(张化光), Gong Da-Wei(宫大为), and Wang Zhan-Shan(王占山) . Chin. Phys. B, 2011, 20(4): 040512.
[15]	Global synchronization of general delayed complex networks with stochastic disturbances Tu Li-Lan(涂俐兰). Chin. Phys. B, 2011, 20(3): 030504.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Metrics
Related Articles

Novel delay-distribution-dependent stability analysis for continuous-time recurrent neural networks with stochastic delay

Cite this article:

Online attention