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Neural Processing Letters
Erschienen in: Neural Processing Letters 5/2021

07.07.2021

Synchronization Control for Chaotic Neural Networks with Mixed Delays Under Input Saturations

verfasst von: Liuyuan Chen, Yonggang Chen, Nannan Zhang

Erschienen in: Neural Processing Letters | Ausgabe 5/2021

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Abstract

This paper is concerned with the exponential synchronization problem for chaotic neural networks with discrete and distributed delays under input saturations. Attention is focused on the design of saturated feedback controller such that the synchronization error system is locally exponentially stable. Based on delay-dependent sector conditions and augmented Lyapunov–Krasovskii functionals, sufficient conditions are established in the framework of linear matrix inequalities. Moreover, the optimization problems are proposed to maximize the estimate of the domain of attraction. Finally, two simulation examples show the effectiveness and benefit of the obtained results.
Vorheriger Artikel An advanced Hybrid Algorithm for Engineering Design Optimization
Nächster Artikel Observer-Based Adaptive NN Tracking Control for Nonstrict-Feedback Systems with Input Saturation

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Literatur
1.
Aouiti C, Li X, Miaadi F (2019) A new LMI approach to finite and fixed time stabilization of high-order class of BAM neural networks with time-varying delays. Neural Process Lett 50:815–838
2.
Baldi P, Atiya AF (1994) How delays affect neural dynamics and learning. IEEE Trans Neural Netw 5(4):612–621
3.
Cao J, Lu J (2006) Adaptive synchronization of neural networks with or without time-varying delay. Chaos 16(1):013133MathSciNetMATH
4.
Chen G, Dong X (1998) From Chaos to order: methodologies, perspectives, and applications. World Scientific, SingaporeMATH
5.
Chen G, Sun J, Xia J (2020) Estimation of domain of attraction for aperiodic sampled-data switched delayed neural networks subject to actuator saturation. IEEE Trans Neural Netw Learn Syst 31(5):1489–1503MathSciNet
6.
Chen Y, Fei S, Li Y (2017) Robust stabilization for uncertain saturated time-delay systems: a distributed-delay-dependent polytopic approach. IEEE Trans Autom Control 62(7):3455–3460MathSciNetMATH
7.
Chen Y, Wang Z, Liu Y, Alsaadi FE (2018) Stochastic stability for distributed delay neural networks via augmented Lyapunov–Krasovskii functionals. Appl Math Comput 338:869–881MathSciNetMATH
8.
Chen Y, Wang Z, Shen B, Dong H (2019) Exponential synchronization for delayed dynamical networks via intermittent control: dealing with actuator saturations. IEEE Trans Neural Netw Learn Syst 30(4):1000–1013MathSciNet
9.
Chen Y, Wang Z, Han QL, Hu J (2021) Synchronization control for discrete-time delayed dynamical networks with switching topology under actuator saturations. IEEE Trans Neural Netw Learn Syst 32(5):2040–2053MathSciNet
10.
Fridman E, Pila A, Shaked U (2003) Regional stabilization and \(H_\infty \) control of time-delay systems with saturating actuators. Int J Robust Nonlin Control 13(9):885–907MathSciNetMATH
11.
Gilli M (1993) Strange attractors in delayed cellular neural networks. IEEE Trans Circuits Syst 1: Fundam Theory Appl 40(11):849–853MathSciNetMATH
12.
Gopalsamy K, He XZ (1994) Stability in asymmetric Hopfield nets with transmission delays. Phys D: Nonlinear Phenom 76(4):344–358MathSciNetMATH
13.
Gu Z, Shi P, Yue D, Ding Z (2019) Decentralized adaptive event-triggered \(H_\infty \) filtering for a class of networked nonlinear interconnected systems. IEEE Trans Cybern 49(5):1570–1579
14.
He Y, Ji MD, Zhang CK, Wu M (2016) Global exponential stability of neural networks with time-varying delay based on free-matrix-based integral inequality. Neural Netw 77:80–86MATH
15.
Karthick SA, Sakthivel R, Wang C, Ma YK (2020) Synchronization of coupled memristive neural networks with actuator saturation and switching topology. Neurocomputing 383:138–150
16.
Liu H, Wang Z, Shen B, Dong H (2020) Delay-distribution-dependent \(H_\infty \) state estimation for discrete-time memristive neural networks with mixed time-delays and fading measurements. IEEE Trans Cybern 50(2):440–451
17.
18.
Li JN, Bao WD, Li SB et al (2016) Exponential synchronization of discrete-time mixed delay neural networks with actuator constraints and stochastic missing data. Neurocomputing 207:700–707
19.
Li X (2010) Global robust stability for stochastic interval neural networks with continuously distributed delays of neutral type. Appl Math Comput 215(12):4370–4384MathSciNetMATH
20.
Lian HH, Xiao SP, Yan H, Yang F, Zeng HB (2021) Dissipativity analysis for neural networks with time-varying delays via a delay-product-type Lyapunov functional approach. IEEE Trans Neural Netw Learn Syst 32(3):975–984MathSciNet
21.
Lin Z (1999) Low gain feedback. Springer, London
22.
Lu H (2002) Chaotic attractors in delayed neural networks. Phys Lett A 298(2–3):109–116MATH
23.
Ma L, Wang Z, Liu Y, Alsaadi FE (2017) A note on guaranteed cost control for nonlinear stochastic systems with input saturation and mixed time-delays. Int J Robust Nonlin Control 27(18):4443–4456MathSciNetMATH
24.
Ma L, Wang Z, Liu Y, Alsaadi FE (2019) Distributed filtering for nonlinear time-delay systems over sensor networks subject to multiplicative link noises and switching topology. Int J Robust Nonlin Control 29(10):2941–2959MathSciNetMATH
25.
Mu X, Chen Y (2016) Synchronization of delayed discrete-time neural networks subject to saturated time-delay feedback. Neurocomputing 175:293–299
26.
Ouyang D, Shao J, Jiang H, Nguang SK, Shen HT (2020) Impulsive synchronization of coupled delayed neural networks with actuator saturation and its application to image encryption. Neural Netw 128:158–171MATH
27.
Pecora LM, Carroll TL, Johnson GA et al (1997) Fundamentals of synchronization in chaotic systems, concepts, and applications. Chaos 7(4):520–543MathSciNetMATH
28.
Qian W, Li Y, Zhao Y, Chen Y (2020) New optimal method for \(L_2\)-\(L_\infty \) state estimation of delayed neural networks. Neurocomputing 415:258–265
29.
30.
Qian W, Li Y, Chen Y, Liu W (2020) \(L_2\)-\(L_\infty \) filtering for stochastic delayed systems with randomly occurring nonlinearities and sensor saturation. Int J Syst Sci 51(13):2360–2377
31.
Selvaraj P, Sakthivel R, Kwon OM (2018) Finite-time synchronization of stochastic coupled neural networks subject to Markovian switching and input saturation. Neural Netw 105:154–165MATH
32.
Selvaraj P, Sakthivel R, Kwon OM (2018) Synchronization of fractional-order complex dynamical network with random coupling delay, actuator faults and saturation. Nonlinear Dyn 94(4):3101–3116MATH
33.
Seuret A, Gouaisbaut F (2013) Wirtinger-based integral inequality: application to time-delay systems. Automatica 49(9):2860–2866MathSciNetMATH
34.
Sweetha S, Sakthivel R, Harshavarthini S (2021) Finite-time synchronization of nonlinear fractional chaotic systems with stochastic actuator faults. Chaos, Solitons & Fractals 142:110312MathSciNet
35.
Tarbouriech S, Garcia G, da Silva Gomes JM Jr, Queinnec I (2011) Stability and stabilization of linear systems with saturating actuators. Springer, LondonMATH
36.
Tourani S, Rahmani Z, Rezaie B (2016) Adaptive observer-based projective synchronization for chaotic neural networks with mixed time delays. Chin J Phys 54(2):285–297MathSciNet
37.
Wu ZG, Shi P, Su H, Chu J (2012) Exponential synchronization of neural networks with discrete and distributed delays under time-varying sampling. IEEE Trans Neural Netw Learn Syst 23(9):1368–1376
38.
Wu ZG, Shi P, Su H, Chu J (2014) Local synchronization of chaotic neural networks with sampled-data and saturating actuators. IEEE Trans Cybern 44(12):2635–2645
39.
Xiao SP, Lian HH, Teo KL, Zeng HB, Zhang XH (2018) A new Lyapunov functional approach to sampled-data synchronization control for delayed neural networks. J Frankl Inst 355(17):8857–8873MathSciNetMATH
40.
Xiong X, Tang R, Yang X (2019) Finite-time synchronization of memristive neural networks with proportional delay. Neural Process Lett 50:1139–1152
41.
Zeng HB, Teo KL, He Y, Xu H, Wang W (2017) Sampled-data synchronization control for chaotic neural networks subject to actuator saturation. Neurocomputing 260:25–31
42.
Zeng HB, Liu XG, Wang W (2019) A generalized free-matrix-based integral inequality for stability analysis of time-varying delay systems. Appl Math Comput 354:1–8MathSciNetMATH
43.
44.
Zeng HB, Lin HC, He Y, Teo KL, Wang W (2020) Hierarchical stability conditions for time-varying delay systems via an extended reciprocally convex quadratic inequality. J Frankl Inst 357:9930–9941MathSciNetMATH
45.
Zeng HB, Zhai ZL, He Y, Teo KL, Wang W (2020) New insights on stability of sampled-data systems with time-delay. Appl Math Comput 374:374MathSciNetMATH
46.
Zhang CK, He Y, Jiang L, Wu M, Wang Q-G (2017) An extended reciprocally convex matrix inequality for stability analysis of systems with time-varying delay. Automatica 85:481–485MathSciNetMATH
47.
Zhang CK, Long F, He Y, Yao W, Jiang L, Wu M (2020) A relaxed quadratic function negative-determination lemma and its application to time-delay systems. Automatica 113:108764MathSciNetMATH
48.
Zhang X, Yuan Y, Li X (2020) Finite-time stabilization for static neural networks with leakage delay and time-varying delay. Neural Process Lett 51:67–81
49.
Zhang XM, Han QL, Seuret A, Gouaisbaut F (2017) An improved reciprocally convex inequality and an augmented Lyapunov–Krasovskii functional for stability of linear systems with time-varying delay. Automatica 84:221–226MathSciNetMATH
50.
Zhang XM, Han QL, Wang Z, Zhang BL (2017) Neuronal state estimation for neural networks with two additive time-varying delay. IEEE Trans Cybern 47(10):3184–3194
51.
Zhang XM, Han QL, Wang J (2018) Admissible delay upper bounds for global asymptotic stability of neural networks with time-varying delays. IEEE Trans Neural Netw Learn Syst 29(11):5319–5329MathSciNet
52.
Zhou B, Lin Z, Duan GR (2010) Global and semi-global stabilization of linear systems with multiple delays and saturations in the input. SIAM J Control Optim 48(8):5294–5332MathSciNetMATH
Metadaten
Titel
Synchronization Control for Chaotic Neural Networks with Mixed Delays Under Input Saturations
verfasst von
Liuyuan Chen
Yonggang Chen
Nannan Zhang
Publikationsdatum
07.07.2021
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 5/2021
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10577-9

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