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Research Papers

Compound Synchronization Based on Memristive Cellular Neural Network of Chaos System

[+] Author and Article Information
Xiaohong Zhang

School of Information Engineering,
Jiangxi University of Science and Technology,
Ganzhou 341000, China
e-mail: xiaohongzh@263.net

Linyu Liao

School of Information Engineering,
Jiangxi University of Science and Technology,
Ganzhou 341000, China

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS. Manuscript received July 15, 2015; final manuscript received August 14, 2016; published online December 5, 2016. Assoc. Editor: Bogdan I. Epureanu.

J. Comput. Nonlinear Dynam 12(3), 031002 (Dec 05, 2016) (9 pages) Paper No: CND-15-1218; doi: 10.1115/1.4034565 History: Received July 15, 2015; Revised August 14, 2016

As a especial type of synchronous method, compound synchronization is designed by multiple drive systems and response systems. In this paper, a new type of compound synchronization of three drive systems and two response systems is investigated. According to synchronous control of five memristive cellular neural networks (CNNs), the theoretical analysis and demonstration are given out by using Lyapunov stability theory. The corresponding numerical simulations and synchronous performance analysis are supplied to verify the feasibility and scalability of compound synchronization design.

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References

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Figures

Grahic Jump Location
Fig. 1

Compound synchronization scheme of five systems

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Fig. 2

Compound synchronization summary of Corollaries 1–8

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Fig. 3

The chaotic attractors from system (14)

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Fig. 4

Waveforms of combination x(x2i+x3i)1i and y1i+y2i(i=1,2,3) between drive systems (15) and response systems (16): (a) x(x21+x31)11 and y11+y21, (b) x(x22+x32)12 and y12+y22, and (c) x(x23+x33)13 and y13+y23

Grahic Jump Location
Fig. 5

Synchronization error curves with e1,e2,e3

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