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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2017

06.04.2017

Adaptive Synchronization of Stochastic Memristor-Based Neural Networks with Mixed Delays

verfasst von: Yinfang Song, Wen Sun

Erschienen in: Neural Processing Letters | Ausgabe 3/2017

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Abstract

In this paper, adaptive synchronization of stochastic memristor-based neural networks with mixed delays is investigated. By using the differential inclusions theory, adaptive control technique and stochastic Lyapunov method, two adaptive updated laws are designed and two synchronization criteria are derived for stochastic memristor-based neural networks with mixed delays. The derived criteria complement and improve the previously known results since stochastic perturbations and mixed delays are considered. Finally, two numerical examples are provided to illustrate the effectiveness of the theoretical results.
Vorheriger Artikel Convergence of Quasi-Newton Method for Fully Complex-Valued Neural Networks
Nächster Artikel Asymptotic Stability of Cohen–Grossberg BAM Neutral Type Neural Networks with Distributed Time Varying Delays

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Literatur
1.
Chua L (1971) Memristor-the missing circuit element. IEEE Trans Circuit Theory 18:507–519CrossRef
2.
Strukov D, Snider G, Stewart D, Williams R (2008) The missing memristor found. Nature 453:80–83CrossRef
3.
Cao J, Lu J (2006) Adaptive synchronization of neural networks with or without time-varying delay. Chaos 16:121–133MathSciNetMATH
4.
Zhang H, Huang W, Wang Z, Chai T (2006) Adaptive synchronization between two different chaotic systems with unknown parameters. Phys Lett A 350:363–366CrossRefMATH
5.
Wang Z, Wang Y, Liu Y (2010) Global synchronization for discrete-time stochastic complex networks with randomly occurred nonlinearities and mixed time delays. IEEE Trans Neural Netw 21:11–25CrossRef
6.
Liu Y, Wang Z, Liang J, Liu X (2009) Stability and synchronization of discerete-time Markovian jumping neural networks with mixed mode-dependent time delays. IEEE Trans Neural Netw 20:1102–1116CrossRef
7.
Lu W, Chen T (2004) Synchronization of coupled connected neural networks with delays. IEEE Trans Circuits Syst I Regul Pap 51:2491–2504MathSciNetCrossRefMATH
8.
Shen Y, Wang J (2012) Robust analysis of global exponential stability of recurrent neural networks in the presence of time delays and random disturbances. IEEE Trans Neural Netw Learn Syst 23:87–96CrossRef
9.
Wang K, Teng Z, Jiang H (2008) Adaptive synchronization of neural networks with time-varying delay and distributed delay. Phys A 387:631–642CrossRef
10.
Li X, Cao J (2008) Adaptive synchronization for delayed neural networks with stochastic perturbation. J Frankl Inst 345:779–791MathSciNetCrossRefMATH
11.
Zhu Q, Cao J (2011) Adaptive synchronization under almost every initial data for stochastic neural networks with time-varying delays and distributed delays. Commun Nonlinear Sci Numer Simul 16:2139–2159MathSciNetCrossRefMATH
12.
Wu A, Zeng Z, Zhu X, Zhang J (2011) Exponential synchronization of memristor-based recurrent networks with timedelays. Neurocomputing 74:3043–3053CrossRef
13.
Wu A, Wen S, Zeng Z (2012) Synchronization control of a class of memristor-based recurrent neural networks. Inf Sci 183:106–116MathSciNetCrossRefMATH
14.
Wen S, Gang Bao, Zeng Z, Chen Y, Huang T (2013) Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays. Neural Netw 48:195–203CrossRefMATH
15.
Yang X, Cao J, Long Y, Rui W (2010) Adaptive lag synchronization for competitive neural networks with mixed delays and uncertain hybrid perturbations. IEEE Trans Neural Netw 21(10):1656–1667CrossRef
16.
Lu J, Cao J (2008) Adaptive synchronization of uncertain dynamical networks with delay coupling. Nonlinear Dyn 53:107–115CrossRefMATH
17.
Li N, Cao J (2015) New synchronization criteria for memristor-based networks: adaptive control and feedback control schemes. Neural Netw 61:1–9CrossRefMATH
18.
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 24:1701–1707MathSciNet
19.
Zhang G, Shen Y (2014) Exponential synchronization of delayed memristor-based chaotic neural networks via periodically intermittent control. Neural Netw 55:1–10CrossRefMATH
20.
Cai Z, Huang L, Wang D, Zhang L (2015) Periodic synchronization in delayed memristive neural networks based on Filippov systems. J Frankl Inst 352:4638–4663MathSciNetCrossRef
21.
Guo Z, Wang J, Yan Z (2014) Passivity and passification of memristor-based recurrent neural networks with time-varying delays. IEEE Trans Neural Netw Learn Syst 25:2099–2109CrossRef
22.
Zhang W, Li C, Huang T, Huang J (2016) Stability and synchronization of memristor-based coupling neural networks with time-varying delays via intermittent control. Neurocomputing 173:1066–1072CrossRef
23.
Abdurahman A, Jiang H, Teng Z (2015) Finite-time synchronization for memristor-based neural networks with time-varying delays. Neural Netw 63:1–9CrossRef
24.
Bao H, Park JH, Cao J (2016) Matrix measure strategies for exponential synchronization and anti-synchronization of memristor-based neural networks with time-varying delays. Appl Math Comput 142:143–154
25.
Wang W, Peng H, Li L, Xiao J, Yang J (2013) Finite-time function projective synchronization in complex multi-links networks with time-varying delay. Neural Process Lett. doi:10.​1007/​s11063-013-9335-4
26.
Wu H, Li R, Zhang X, Yao R (2015) Adaptive finite-time complete periodic synchronization of memristive neural networks with time delays. Neural Process Lett. doi:10.​1007/​/​s11063-014-9373-6
27.
Abdurahman A, Jiang H, Rahman K (2015) Function projective synchronization of memristor-based Cohen–Grossberg neural networks with time-varying delays. Cogn Neurodyn 9:603–613CrossRef
28.
Du H, Wen G, Chen Y, He Y, Jia R (2016) Distributed finite-time cooperative control of multiple high-order nonholonomic mobile robots. IEEE Trans Neural Netw Learn Syst. doi:10.​1109/​TNNLS.​2016.​2610140
29.
Du H, Li S (2016) Attitude synchronization for flexible spacecraft with communication delays. IEEE Trans Autom Control 61(11):3625–3630MathSciNetCrossRefMATH
30.
Du H, Li S, Qian C (2011) Finite-time attitude tracking control of spacecraft with application to attitude synchronization. IEEE Trans Autom Control 56(11):2711–2717MathSciNetCrossRefMATH
31.
Li S, Du H, Lin X (2011) Finite time consensus algorithm for multi-agent systems with double integrator dynamics. Automatica 47(8):1706–1712MathSciNetCrossRefMATH
32.
Haykin S (1994) Neural networks: a comprehensive foundation. Prentice Hall, New JerseyMATH
33.
Song Y, Wen S (2015) Synchronization control of stochastic memristor-based neural networks with mixed delays. Neurocomputing 156:121–128CrossRef
34.
Ding S, Wang Z (2015) Stochastic exponential synchronization control of memristive neural networks with multiple time-varying delays. Neurocomputing 162:16–25CrossRef
35.
Yang X, Cao J, Qiu J (2015) pth moment exponential stochastic synchronization of coupled memristor-based neural networks with mixed delays via delayed impulsive control. Neural Netw 65:80–91CrossRef
36.
Chandrasekar A, Rakkiyappan R (2016) Impulsive controller design for exponential synchronization of delayed stochastic memristor-based recurrent neural networks. Nerocomputing 173:1348–1355CrossRef
37.
Wang W, Li L, Peng H, Kurths J, Xiao J, Yang Y (2014) Finite-time anty-synchronization control of memrsitive neural networks with stochastic perturbations. Neural Process Lett. doi:10.​1007/​s11063-014-9401-6
38.
Zhao H, Li L, Peng H, Kurths J, Xiao J, Yang Y (2015) Anti-synchronization for stochastic memristor-based neural networks with non-modeled dynamics via adaptive control approach. Eur Phys J B 88:2–10
39.
Yang X, Cao J (2014) Hybrid adaptive and impulsive synchronization of uncertain complex networks with delays and general uncertain perturbations. Apple Math Comput 227(15):480–493MathSciNetCrossRefMATH
40.
Yang X, Cao J, Yu W (2014) Exponential synchronization of memristive Cohen–Grossberg neural networks with mixed delays. Cogn Neurodyn 8:239–249CrossRef
41.
42.
43.
Chandrasekar A, Rakkiyappan R, Cao J, Lakshmanan 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
44.
Rakkiyappan R, Chandrasekar A, Cao J (2015) Passivity and passification of memristor-based recurrent neural networks with additive time-varying delays. IEEE Trans Neural Netw 26(9):2043–2057MathSciNetCrossRef
45.
Rakkiyappan R, Velmurugan G, Cao J (2015) Stability analysis of memristor-based fractional-order neural networks with different memductance functions. Cognit Neurodyn 9:145–177CrossRef
46.
Velmurugan G, Rakkiyappan R, Cao J (2016) Finite-time synchronization of fractional-order memristor-based neural networks with time delays. Neural Netw 73:36–46CrossRefMATH
47.
Lu J (2012) Pinning impulsive stabilization of nonlinear dynamical networks with time-varying delay. Int J Bifurc Chaos 22(7):1250176CrossRefMATH
48.
Yang X, Lu J (2016) Finite-time synchronization of coupled networks with Markvoian topology and impulsive effects. IEEE Trans Autom Control 61:2256–2261CrossRefMATH
Metadaten
Titel
Adaptive Synchronization of Stochastic Memristor-Based Neural Networks with Mixed Delays
verfasst von
Yinfang Song
Wen Sun
Publikationsdatum
06.04.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2017
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9623-5

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