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Neural Computing and Applications

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12.04.2016 | Original Article

Lag quasi-synchronization for memristive neural networks with switching jumps mismatch

verfasst von: Sanbo Ding, Zhanshan Wang

Erschienen in: Neural Computing and Applications | Ausgabe 12/2017

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Abstract

This paper is concerned with the lag quasi-synchronization for memristive neural networks (MNNs) with switching jumps mismatch. The inherent characteristic of MNNs is fully taken into account. Based on Lyapunov–Krasovskii functional and differential inclusions theory, intermittent control approach is utilized to realize the exponential lag quasi-synchronization of the considered model. The error level is closely related to the switching jumps. In addition, a simple design procedure of controller is presented to ensure that the synchronization error between the master system and the slave system converges to a predetermined level. Two numerical examples are offered to show the effectiveness of the proposed method.

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Chua L (1971) Memristor-the missing circuit element. IEEE Trans Circuit Theory 18(5):507–519CrossRef

Strukov DB, Snider GS, Stewart DR, Williams RS (2008) The missing memristor found. Nature 453(7191):80–83CrossRef

Corinto F, Ascoli A, Gilli M (2011) Nonlinear dynamics of memristor oscillators. IEEE Trans Circuits Syst I Regul Pap 58(6):1323–1336MathSciNetCrossRef

Hu M, Li H, Chen Y, Wu Q, Rose G, Linderman R (2014) Memristor crossbar-based neuromorphic computing system: a case study. IEEE Trans Neural Netw Learn Syst 25(10):1864–1878CrossRef

Duan S, Hu X, Dong Z, Wang L, Mazumder P (2015) Memristor-based cellular nonlinear/neural network: design, analysis, and applications. IEEE Trans Neural Netw Learn Syst 26(6):1202–1213MathSciNetCrossRef

Wang Z, Zhang H, Jiang B (2011) LMI-based approach for global asymptotic stability analysis of recurrent neural networks with various delays and structures. IEEE Trans Neural Netw 22(7):1032–1045CrossRef

Wang Z, Liu L, Shan Q, Zhang H (2015) Stability criteria for recurrent neural networks with time-varying delay based on secondary delay partitioning method. IEEE Trans Neural Netw Learn Syst 26(10):2589–2595MathSciNetCrossRef

Wang Z, Zhang H (2010) Global asymptotic stability of reaction-diffusion Cohen–Grossberg neural networks with continuously distributed delays. IEEE Trans Neural Netw 21(1):39–49CrossRef

Wang Z, Ding S, Huang Z, Zhang H (2015) Exponential stability and stabilization of delayed memristive neural networks based on quadratic convex combination method. IEEE Trans Neural Netw Learn Syst. doi:10.1109/TNNLS.2015.2485259

10.

Guo Z, Wang J, Yan Z (2014) Attractivity analysis of memristor-based cellular neural networks with time-varying delays. IEEE Trans Neural Netw Learn Syst 25(4):704–717CrossRef

11.

Li R, Cao J (2015) Dissipativity analysis of memristive neural networks with time-varying delays and randomly occurring uncertainties. Math Meth Appl Sci. doi:10.1002/mma.3738 MATH

12.

Wen S, Zeng Z, Huang T, Zhang Y (2014) Exponential adaptive lag synchronization of memristive neural networks via fuzzy method and applications in pseudo random number generator. IEEE Trans Fuzzy Syst 22(6):1704–1713CrossRef

13.

Zhang G, Hu J, Shen Y (2015) Exponential lag synchronization for delayed memristive recurrent neural networks. Neurocomputing 154:86–93CrossRef

14.

Wen S, Zeng Z, Huang T (2013) Dynamic behaviors of memristor-based delayed recurrent networks. Neural Comput Appl 23(3–4):815–821CrossRef

15.

Wu A, Zeng Z, Chen J (2014) Analysis and design of winner-take-all behavior based on a novel memristive neural network. Neural Comput Appl 24(7–8):1595–1600CrossRef

16.

Chen J, Zeng Z, Jiang P (2014) On the periodic dynamics of a memristor-based neural networks with time-varying delays. Inf Sci 279(20):358–373MathSciNetCrossRefMATH

17.

Wang X, Li C, Huang T, Duan S (2014) Global exponential stability of a class of memristive neural networks with time-varying delays. Neural Comput Appl 24(7–8):1707–1715CrossRef

18.

Mathiyalagan K, Anbuvithya R, Sakthivel R, Park J, Prakash P (2015) Reliable stabilization for memristor-based recurrent neural networks with time-varying delays. Neurocomputing 153:140–147CrossRef

19.

Guo Z, Wang J, Yan Z (2015) Global exponential synchronization of two memristor-based recurrent neural networks with time delays via static or dynamic coupling. IEEE Trans Syst Man Cyber Syst 45(2):235–249CrossRef

20.

Ding S, Wang Z (2015) Stochastic exponential synchronization control of memristive neural networks with multiple time-varying delays. Neurocomputing 162:16–25CrossRef

21.

Wu H, Zhang L, Ding S, Guo X, Wang L (2013) Complete periodic synchronization of memristor-based neural networks with time-varying delays. Discrete Dyn Nat Soc 2013:1–12, ID 140153

22.

Wang G, Shen Y (2014) Exponential synchronization of coupled memristive neural networks with time delays. Neural Comput Appl 24(6):1421–1430CrossRef

23.

Zhang G, Shen Y (2014) Exponential synchronization of delayed memristor-based chaotic neural networks via periodically intermittent control. Neural Netw 55:1–10CrossRefMATH

24.

Wang L, Shen Y, Yin Q, Zhang G (2015) Adaptive synchronization of memristor-based neural networks with time-varying delays. IEEE Trans Neural Netw Learn Syst 26(9):2033–2042MathSciNetCrossRef

25.

Chandrasekar A, Rakkiyappan R, Cao J, Lakshmanand S (2014) Synchronization of memristor-based recurrent neural networks with two delay components based on second-order reciprocally convex approach. Neural Netw 57:79–93CrossRefMATH

26.

Mathiyalagan K, Park J, Sakthivel R (2015) Synchronization for delayed memristive BAM neural networks using impulsive control with random nonlinearities. Appl Math Comput 259:967–979MathSciNet

27.

Anbuvithya R, Mathiyalagan K, Sakthivel R, Prakasha P (2015) Non-fragile synchronization of memristive BAM networks with random feedback gain fluctuations. Commun Nonlinear Sci Numer Simul 29(1–3):427–440MathSciNetCrossRef

28.

Wang W, Li L, Peng H, Xiao J, Yang Y (2014) Synchronization control of memristor-based recurrent neural networks with perturbations. Neural Netw 53:8–14CrossRefMATH

29.

Liu X, Chen T, Cao J, Lu W (2011) Dissipativity and quasi-synchronization for neural networks with discontinuous activations and parameter mismatches. Neural Netw 24(10):1013–1021CrossRefMATH

30.

Yang X, Cao J (2013) Exponential synchronization of delayed neural networks with discontinuous activations. IEEE Trans Circuits Syst I 60(9):2431–2439MathSciNetCrossRef

31.

Chua L (2011) Resistance switching memories are memristors. Appl Phys A 102:765–783CrossRefMATH

32.

Li N, Cao J (2014) Periodically intermittent control on robust exponential synchronization for switched interval coupled networks. Neurocomputing 131:52–58CrossRef

33.

Huang J, Li C, Huang T, He X (2014) Finite-time lag synchronization of delayed neural networks. Neurocomputing 139(2):145–149CrossRef

34.

Zhang H, Ma T, Yu W, Fu J (2008) A practical approach to robust impulsive lag synchronization between different chaotic systems. Chin Phys B 17(10):3616–3622CrossRef

35.

He W, Qian F, Han Q, Cao J (2011) Lag quasi-synchronization of coupled delayed systems with parameter mismatch. IEEE Trans Circuits Syst I 58(6):1345–1357MathSciNetCrossRef

36.

Clarke F, Ledyaev Y, Stem R, Wolenski R (1998) Nonsmooth analysis and control theory. Springer, New York

37.

Aubin J, Cellina A (1984) Differential inclusions. Springer, BerlinCrossRefMATH

38.

Filippov A (1988) Differential equations with discontinuous righthand sides. Kluwer Academic Publishers, BostonCrossRef

39.

Vidyasagar M (1993) Nonlinear system analysis. Prentice-Hall, Englewood CliffsMATH

40.

Sakthivel R, Anbuvithya R, Mathiyalagan K, Prakash P (2015) Combined \(H_\infty\) and passivity state estimation of memristive neural networks with random gain fluctuations. Neurocomputing 168:1111–1120CrossRef

Titel: Lag quasi-synchronization for memristive neural networks with switching jumps mismatch
verfasst von: Sanbo Ding
Zhanshan Wang
Publikationsdatum: 12.04.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 12/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2291-y

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