Please wait a minute...
Chin. Phys. B, 2010, Vol. 19(11): 110512    DOI: 10.1088/1674-1056/19/11/110512
GENERAL Prev   Next  

An linear matrix inequality approach to global synchronisation of non-parameter perturbations of multi-delay Hopfield neural network

Shao Hai-Jian(邵海见), Cai Guo-Liang(蔡国梁), and Wang Hao-Xiang(汪浩祥)
Nonlinear Scientific Research Center, Jiangsu University, Zhenjiang 212013, China
Abstract  In this study, a successful linear matrix inequality approach is used to analyse a non-parameter perturbation of multi-delay Hopfield neural network by constructing an appropriate Lyapunov-Krasovskii functional. This paper presents the comprehensive discussion of the approach and also extensive applications.
Keywords:  Hopfield neural network      LMI approach      global synchronisation      sliding mode control  
Received:  12 March 2010      Revised:  26 May 2010      Accepted manuscript online: 
PACS:  02.10.Yn (Matrix theory)  
  07.05.Dz (Control systems)  
Fund: Project supported by the National Natural Science Foundations of China (Grant Nos. 70571030 and 90610031), the Society Science Foundation from Ministry of Education of China (Grant No. 08JA790057) and the Advanced Talents' Foundation and Student's Foundation of Jiangsu University (Grant Nos. 07JDG054 and 07A075).

Cite this article: 

Shao Hai-Jian(邵海见), Cai Guo-Liang(蔡国梁), and Wang Hao-Xiang(汪浩祥) An linear matrix inequality approach to global synchronisation of non-parameter perturbations of multi-delay Hopfield neural network 2010 Chin. Phys. B 19 110512

[1] Hopfield J J 1982 Proc. Nation. Acad. Sci. USA 79 2554
[2] Zhang J Y 2003 Appli. Math. Lett. 6 925
[3] Wang R L, Tang Z and Cao Q P 2002 Neur. Comp. 48 1021
[4] Dan S 1993 IEEE Trans. Circ. Sys. II 11 745
[5] Liu D and Lu Z 1997 IEEE Trans. Neur. Netw. 12 1468
[6] Gao M and Cui B T 2009 Chin. Phys. B 18 76
[7] Wu R C 2009 Acta Phys. Sin. 58 139 (in Chinese)
[8] Cai G L and Shao H J 2010 Chin. Phys. B 19 060507
[9] Qiu F, Cui B T and Ji Y 2009 Chin. Phys. B 18 5203
[10] Lou X Y and Cui B T 2007 Int. J. Auto. Comp. 3 304
[11] Tang Y, Zhong H H and Fang J A 2008 Chin. Phys. B 17 4080
[12] Liao X, Wonh K, Wu Z and Chen G 2001 IEEE Trans. Circ. Sys. 48 1355
[13] Wang S, Cai L, Kang Q, Wu G and Li Q 2008 Chin. Phys. B 17 2837
[14] Lou X Y and Cui B T 2008 Chin. Phys. B 17 520
[15] Alonso H G, Mendoncca T, Rocha P 2009 Neur. Netw. 4 450
[16] Liu F C and Song J Q 2008 Acta Phys. Sin. 57 4729 (in Chinese)
[17] Young K D, Utkin V I and "Ozg"uner "U A 1999 IEEE Trans. Cont. Sys. Tech. 3 328
[18] Huang H and Gang F 2009 Neur. Netw. Lett. 22 869 endfootnotesize
[1] FPGA implementation and image encryption application of a new PRNG based on a memristive Hopfield neural network with a special activation gradient
Fei Yu(余飞), Zinan Zhang(张梓楠), Hui Shen(沈辉), Yuanyuan Huang(黄园媛), Shuo Cai(蔡烁), and Sichun Du(杜四春). Chin. Phys. B, 2022, 31(2): 020505.
[2] Fixed time integral sliding mode controller and its application to the suppression of chaotic oscillation in power system
Jiang-Bin Wang(王江彬), Chong-Xin Liu(刘崇新), Yan Wang(王琰), Guang-Chao Zheng(郑广超). Chin. Phys. B, 2018, 27(7): 070503.
[3] A new four-dimensional chaotic system with first Lyapunov exponent of about 22, hyperbolic curve and circular paraboloid types of equilibria and its switching synchronization by an adaptive global integral sliding mode control
Jay Prakash Singh, Binoy Krishna Roy, Zhouchao Wei(魏周超). Chin. Phys. B, 2018, 27(4): 040503.
[4] Finite-time robust control of uncertain fractional-order Hopfield neural networks via sliding mode control
Yangui Xi(喜彦贵), Yongguang Yu(于永光), Shuo Zhang(张硕), Xudong Hai(海旭东). Chin. Phys. B, 2018, 27(1): 010202.
[5] Dynamic analysis and fractional-order adaptive sliding mode control for a novel fractional-order ferroresonance system
Ningning Yang(杨宁宁), Yuchao Han(韩宇超), Chaojun Wu(吴朝俊), Rong Jia(贾嵘), Chongxin Liu(刘崇新). Chin. Phys. B, 2017, 26(8): 080503.
[6] Robust pre-specified time synchronization of chaotic systems by employing time-varying switching surfaces in the sliding mode control scheme
Alireza Khanzadeh, Mahdi Pourgholi. Chin. Phys. B, 2016, 25(8): 080501.
[7] Controlling chaos based on a novel intelligent integral terminal sliding mode control in a rod-type plasma torch
Safa Khari, Zahra Rahmani, Behrooz Rezaie. Chin. Phys. B, 2016, 25(5): 050201.
[8] Robust sliding mode control for fractional-order chaotic economical system with parameter uncertainty and external disturbance
Zhou Ke (周柯), Wang Zhi-Hui (王智慧), Gao Li-Ke (高立克), Sun Yue (孙跃), Ma Tie-Dong (马铁东). Chin. Phys. B, 2015, 24(3): 030504.
[9] Full-order sliding mode control of uncertain chaos in a permanent magnet synchronous motor based on a fuzzy extended state observer
Chen Qiang (陈强), Nan Yu-Rong (南余荣), Zheng Heng-Huo (郑恒火), Ren Xue-Mei (任雪梅). Chin. Phys. B, 2015, 24(11): 110504.
[10] Finite-time sliding mode synchronization of chaotic systems
Ni Jun-Kang (倪骏康), Liu Chong-Xin (刘崇新), Liu Kai (刘凯), Liu Ling (刘凌). Chin. Phys. B, 2014, 23(10): 100504.
[11] Generalized projective synchronization of the fractional-order chaotic system using adaptive fuzzy sliding mode control
Wang Li-Ming (王立明), Tang Yong-Guang (唐永光), Chai Yong-Quan (柴永泉), Wu Feng (吴峰). Chin. Phys. B, 2014, 23(10): 100501.
[12] Synchronization of uncertain fractional-order chaotic systems with disturbance based on fractional terminal sliding mode controller
Wang Dong-Feng (王东风), Zhang Jin-Ying (张金营), Wang Xiao-Yan (王晓燕). Chin. Phys. B, 2013, 22(4): 040507.
[13] Chaos synchronization of a chain network based on a sliding mode control
Liu Shuang (柳爽), Chen Li-Qun (陈立群). Chin. Phys. B, 2013, 22(10): 100506.
[14] Adaptive projective synchronization of different chaotic systems with nonlinearity inputs
Niu Yu-Jun(牛玉军), Wang Xing-Yuan(王兴元), and Pei Bing-Nan(裴炳南) . Chin. Phys. B, 2012, 21(3): 030503.
[15] Spatiotemporal chaos synchronization of an uncertain network based on sliding mode control
Lü Ling (吕翎), Yu Miao (于淼), Wei Lin-Ling (韦琳玲) , Zhang Meng(张檬), Li Yu-Shan (李雨珊). Chin. Phys. B, 2012, 21(10): 100507.
No Suggested Reading articles found!