INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
Prev
Next
|
|
|
Stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks with mixed delays and Wiener process based on sampled-data control |
M. Kalpana, P. Balasubramaniam |
Department of Mathematics, Gandhigram Rural Institute, Deemed University, Gandhigram 624 302, Tamilnadu, India |
|
|
Abstract We investigate the stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks (MJFCNNs) with discrete unbounded distributed delays, and Wiener process based on sampled-data control using linear matrix inequality (LMI) approach. Lyapunov-Krasovskii functional (LKF) combining with the input delay approach as well as the free-weighting matrix approach is employed to derive several sufficient criteria in terms of LMIs to ensure the delayed MJFCNNs with the Wiener process is stochastic asymptotical synchronous. Restrictions (e.g., time derivative is smaller than one) are removed to obtain a proposed sampled-data controller. Finally, a numerical example is provided to demonstrate the reliability of the derived results.
|
Received: 17 December 2012
Revised: 24 December 2012
Accepted manuscript online:
|
PACS:
|
84.35.+i
|
(Neural networks)
|
|
05.45.Gg
|
(Control of chaos, applications of chaos)
|
|
05.45.Xt
|
(Synchronization; coupled oscillators)
|
|
07.05.Mh
|
(Neural networks, fuzzy logic, artificial intelligence)
|
|
Fund: Project supported by the Ministry of Science and Technology of India (Grant No. DST/Inspire Fellowship/2010/[293]/dt). |
Corresponding Authors:
P. Balasubramaniam
E-mail: balugru@gmail.com
|
Cite this article:
M. Kalpana, P. Balasubramaniam Stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks with mixed delays and Wiener process based on sampled-data control 2013 Chin. Phys. B 22 078401
|
[1] |
Chua L O and Yang L 1988 IEEE Trans. Circuits Syst. 35 1257
|
[2] |
Chua L O and Yang L 1988 IEEE Trans. Circuits Syst. 35 1273
|
[3] |
Su T, Huang M, Hou C and Lin Y 2010 Neural Process. Lett. 32 147
|
[4] |
Li H Q, Liao X F, Li C D, Huang H Y and Li C J 2011 Commun. Nonlinear Sci. Numer. Simul. 16 3746
|
[5] |
Yang T, Yang L B, Wu C W and Chua L O 1996 Proceedings of the IEEE International Workshop on Cellular Neural Networks and Applications (Singapore: World Scientific) p.~181
|
[6] |
Yang T, Yang L B, Wu C W and Chua L O 1996 Proceedings of the IEEE International Workshop on Cellular Neural Networks and Applications (Singapore: World Scientific) p.~225
|
[7] |
Shitong W and Min W 2006 IEEE Trans. Inf. Tech. Biomed. 10 5
|
[8] |
Pecora L M and Carroll T L 1990 Phys. Rev. Lett. 64 821
|
[9] |
Kwon O M, Park J H and Lee S M 2011 Nonlinear Dyn. 63 239
|
[10] |
Moskalenko O I, Koronovskii A A and Hramov A E 2010 Phys. Lett. A 374 2925
|
[11] |
Sundar S and Minai A A 2000 Phys. Rev. Lett. 85 5456
|
[12] |
Wang H, Han Z, Zhang W and Xie Q 2009 Nonlinear Dyn. 57 69
|
[13] |
Yu W W and Cao J D 2007 Physica A 373 252
|
[14] |
Li T, Fei S M, Zhu Q and Cong S 2008 Neurocomputing 71 3005
|
[15] |
Sun Y H, Cao J D and Wang Z D 2007 Neurocomputing 70 2477
|
[16] |
Tang Y, Fang J A and Miao Q Y 2009 Int. J. Neural Syst. 19 43
|
[17] |
Liu Y, Wang Z, Liang J and Liu X 2009 IEEE Trans. Neural Netw. 20 1102
|
[18] |
Park J H 2009 Chaos Soliton. Fract. 42 1299
|
[19] |
Balasubramaniam P, Kalpana M and Rakkiyappan R 2012 Chin. Phys. B 21 048402
|
[20] |
Yu F and Jiang H 2011 Neurocomputing 74 509
|
[21] |
Ping Y and Teng L 2012 Commun. Nonlinear Sci. Numer. Simul. 17 1003
|
[22] |
Yu J, Hu C, Jiang H and Teng Z 2012 Math. Comput. Simul. 82 895
|
[23] |
Gan Q, Xu R and Yang P 2012 Commun. Nonlinear Sci. Numer. Simul. 17 433
|
[24] |
Lu J and Hill D J 2008 IEEE Trans. Circuits Syst. II 55 586
|
[25] |
Zhang C, He Y and Wu M 2010 Neurocomputing 74 265
|
[26] |
Gan Q and Liang Y 2012 J. Franklin Inst. 349 1955
|
[27] |
Li N, Zhang Y, Hu J and Nie Z 2011 Neurocomputing 74 805
|
[28] |
Wu Z G, Park J H, Su H, Song B and Chu J 2012 J. Franklin Inst. 349 2735
|
[29] |
Wu Z G, Park J H, Su H and Chu J 2012 Nonlinear Dyn. 69 2021
|
[30] |
Lee T H, Park J H, Lee S M and Kwon O M 2013 Int. J. Control 86 107
|
[31] |
Haykin S 1994 Neural Networks (New Jersey: Prentice-Hall)
|
[32] |
Tang Y and Fang J 2009 Neurocomputing 72 3253
|
[33] |
Gan Q, Xu R and Yang P 2012 Commun. Nonlinear Sci. Numer. Simul. 17 1862
|
[34] |
Han W, Liu Y and Wang L 2012 Neural Comput. Appl. 21 67
|
[35] |
Fridman E, Seuret A and Richard J P 2004 Automatica 40 1441
|
[36] |
Lou X Y and Cui B T 2007 Fuzzy Sets Syst. 158 2746
|
[37] |
Boyd S, Ghaoui L E, Feron E and Balakrishnan V 1994 Linear Matrix Inequalities in Systems and Control Theory (Philadelphia: SIAM)
|
[38] |
Sanchez E N and Perez J P 1999 IEEE Trans. Circuits Syst. I 46 1395
|
[39] |
Liu Z, Zhang H and Wang Z 2009 Neurocomputing 72 1056
|
[40] |
Gu K 2000 Proceedings of the 39th IEEE Conference on Decision and Control, December 12-15, 2000 Sydney, Australia, p.~2805
|
[41] |
Li T, Fei S and Zhu Q 2009 Nonlinear Anal. Real World Appl. 10 1229
|
No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|