|
|
A novel method of constructing high-dimensional digital chaotic systems on finite-state automata |
Jun Zheng(郑俊)1, Han-Ping Hu(胡汉平)1,2 |
1 School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China; 2 Key Laboratory of Image Information Processing and Intelligent Control, Ministry of Education, Wuhan 430074, China |
|
|
Abstract When chaotic systems are implemented on finite precision machines, it will lead to the problem of dynamical degradation. Aiming at this problem, most previous related works have been proposed to improve the dynamical degradation of low-dimensional chaotic maps. This paper presents a novel method to construct high-dimensional digital chaotic systems in the domain of finite computing precision. The model is proposed by coupling a high-dimensional digital system with a continuous chaotic system. A rigorous proof is given that the controlled digital system is chaotic in the sense of Devaney's definition of chaos. Numerical experimental results for different high-dimensional digital systems indicate that the proposed method can overcome the degradation problem and construct high-dimensional digital chaos with complicated dynamical properties. Based on the construction method, a kind of pseudorandom number generator (PRNG) is also proposed as an application.
|
Received: 18 May 2020
Revised: 07 July 2020
Accepted manuscript online: 15 July 2020
|
PACS:
|
05.40.-a
|
(Fluctuation phenomena, random processes, noise, and Brownian motion)
|
|
05.45.Gg
|
(Control of chaos, applications of chaos)
|
|
05.45.Jn
|
(High-dimensional chaos)
|
|
05.45.Vx
|
(Communication using chaos)
|
|
Fund: Project supported by the National Key R&D Program of China (Grant No. 2017YFB0802000) and the Cryptography Theoretical Research of National Cryptography Development Fund, China (Grant No. MMJJ20170109). |
Corresponding Authors:
Han-Ping Hu
E-mail: husthhh@qq.com
|
Cite this article:
Jun Zheng(郑俊), Han-Ping Hu(胡汉平) A novel method of constructing high-dimensional digital chaotic systems on finite-state automata 2020 Chin. Phys. B 29 090502
|
[1] |
Motter A E and Campbell D K 2013 Phys. Today 66 27
|
[2] |
Li T Y and Yorke J A 1975 Am. Math. Mon. 82 985
|
[3] |
Alvarez G and Li S 2006 Int. J. Bifurcation Chaos 16 2129
|
[4] |
Millerioux G, Amigo J M, Daafouz J, et al. 2008 IEEE Trans. Circuits Syst. I-Regul. Pap. 55 1695
|
[5] |
Ozkaynak F 2018 Nonlinear Dyn. 92 305
|
[6] |
Yin R, Wang J, Yuan J, et al. 2012 Sci. Chin. Inf. Sci. 55 1162
|
[7] |
Gao X J, Cheng M F, Deng L, et al. 2020 Opt. Express 28 10847
|
[8] |
Ozkaynak F 2014 Nonlinear Dyn. 78 2015
|
[9] |
Li S, Chen G, Mou X, et al. 2005 Int. J. Bifurcation Chaos 15 3119
|
[10] |
Kwok H S and Tang W K 2007 Chaos Solitons Fractal 32 1518
|
[11] |
Yang B and Liao X 2017 Sci. Chin. Inf. Sci. 60 022302
|
[12] |
Liu L, Lin J, Miao S, et al. 2017 Int. J. Bifurcation Chaos 27 1750103
|
[13] |
Wang C and Ding Q 2019 Complexity 2019 5942121
|
[14] |
Liu H, Zhang Y, Kadir A, et al. 2019 Appl. Math. Comput. 360 83
|
[15] |
Alawida M, Samsudin A, Teh J S, et al. 2019 Signal Process 160 45
|
[16] |
Antonelli M, De Micco L, Larrondo H A, et al. 2018 Entropy 20 135
|
[17] |
Sun C C, Xu Q C and Sui Y 2013 Chin. Phys. B 22 030507
|
[18] |
Hu H, Xu Y and Zhu Z 2008 Chaos Solitons Fractal. 38 439
|
[19] |
Tutueva A V, Andreev V S, Karimov A I, et al. 2020 Chaos Solitons Fractals. 133 109615
|
[20] |
Moysis L, Tutueva A, Volos C, et al. 2020 Symmetry 12 829
|
[21] |
Guyeux C and Bahi J M 2010 International Joint Conference on neural network (IJCNN), July 18-23, 2010, Barcelona, Spain, pp. 1-7
|
[22] |
Wang Q, Yu S, Guyeux C, et al. 2014 Int. J. Bifurcation Chaos 24 1450128
|
[23] |
Wang Q, Yu S, Guyeux C, et al. 2015 Chin. Phys. B 24 060503
|
[24] |
Alawida M, Teh J S, Samsudin A, et al. 2019 Signal Process. 164 249
|
[25] |
Alawida M, Samsudin A, Teh J S, et al. 2019 Nonlinear Dyn. 98 2403
|
[26] |
Zheng J, Hu H, Ming H, et al. 2020 Chaos Solitons Fractals 138 109863
|
[27] |
Lv X, Liao X, Yang B, et al. 2018 Nonlinear Dyn. 94 325
|
[28] |
Fu C, Wen Z K, Zhu Z L, et al. 2016 Int. J. Comput. Sci. Eng. 12 113
|
[29] |
Banks J, Brooks J, Cairns G, et al. 1992 Am. Math. Mon 99 332
|
[30] |
Marwan N, Romano M C, Thiel M, et al. 2007 Phys. Rep. 438 237
|
[31] |
Pincus S M 1991 Proc. Nat Acad. Sci. USA 88 2297
|
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
|
|
|