Stability analysis of fractional-order generalized chaotic susceptible–infected–recovered epidemic model and its synchronization using active control method
- 228 Downloads
- 3 Citations
Abstract
This paper presents the synchronization between a pair of identical susceptible–infected–recovered (SIR) epidemic chaotic systems and fractional-order time derivative using active control method. The fractional derivative is described in Caputo sense. Numerical simulation results show that the method is effective and reliable for synchronizing the fractional-order chaotic systems while it allows the system to remain in chaotic state. The striking features of this paper are: the successful presentation of the stability of the equilibrium state and the revelation that time for synchronization varies with the variation in fractional-order derivatives close to the standard one for different specified values of the parameters of the system.
Keywords
Susceptible–infected–recovered model fractional time derivative stability analysis chaos synchronization active control methodPACS Nos
05.45.−a 05.45.Xt 05.45.PqNotes
Acknowledgements
This work is supported by TCS Research Scholar Programme, Tata Consultancy Services Limited, India. The authors are grateful to the revered reviewers for their pertinent suggestions which have immensely helped them to improve the presentation of this paper.
References
- [1]B Buonomo, A D’Onofrio and D Lacitignola, Math. Biosci. 216, 9 (2008)Google Scholar
- [2]A D’Onofrio, P Manfredi and E Salinelli, Theor. Popul. Biol. 71, 301 (2007)Google Scholar
- [3]A D’Onofrio, P Manfredi and E Salinelli, Math. Model. Nat. Phenom. 2, 23 (2007)Google Scholar
- [4]T K Kar and P K Mondal, Nonlinear Anal. Real World Appl. 12, 2058 (2011)Google Scholar
- [5]L M Pecora and T L Carroll, Phys. Rev. Lett. 64, 821 (1990)Google Scholar
- [6]M C Ho and Y C Hung, Phys. Lett. A 301, 424 (2002)Google Scholar
- [7]J H Park, Chaos, Solitons and Fractals 27, 549 (2006)Google Scholar
- [8]U E Vincent, Nonlinear Anal. Model. Control, 13, 253 (2008)Google Scholar
- [9]S P Ansari and S Das, Math. Meth. Appl. Sci., DOI: 10.1002/mma.3103
- [10]M T Yassen, Chaos, Solitons and Fractals 23, 131 (2005)Google Scholar
- [11]C G Li, X F Liao and J B Yu, Phys. Rev. E 68, 067203 (2003)Google Scholar
- [12]W H Deng and C P Li, Phys. A 353, 61 (2005)Google Scholar
- [13]C P Li and J P Yan, Chaos, Solitons and Fractals 32, 751 (2007)Google Scholar
- [14]J P Yan and C P Li, Chaos, Solitons and Fractals 32, 725 (2007)Google Scholar
- [15]S K Agrawal and S Das, Nonlinear Dyn., DOI: 10.1007/s11071-013-0842-7
- [16]M Srivastava, S K Agrawal and S Das, Int. J. Nonlinear Sci. 13, 482 (2012)Google Scholar
- [17]M Srivastava, S P Ansari, S K Agrawal, S Das and A Y T Leung, Nonlinear Dyn., DOI: 10.1007/s11071-013-1177-0
- [18]S K Agrawal, M Srivastava and S Das, Chaos, Solitons and Fractals 45, 737 (2012)Google Scholar
- [19]D Matignon, Computat. Eng. Syst. App. Lille France 2, 963 (1996)Google Scholar
- [20]E Ahmed, A M A El-Sayed and H A A El-Saka, J. Math. Anal. Appl. 325, 542 (2007)Google Scholar
- [21]L O Chua, M Komuro and T Matsumoto, IEEE Trans. Circ. Syst. 33, 10721118 (1986)Google Scholar
- [22]C P Silva, IEEE Trans. Circ. Syst. I 40, 675682 (1993)Google Scholar
- [23]D Cafagna and G Grassi, Int. J. Bifurcat. Chaos 13, 2889 (2003)Google Scholar
- [24]J Lu, G Chen, X Yu and H Leung, IEEE Trans. Circ. Syst. I 51, 2476 (2004)Google Scholar
- [25]M S Tavazoei and M Haeri, Phys. Lett. A 367 102 (2007)Google Scholar