Finite-time Mittag—Leffler synchronization of fractional-order complex-valued memristive neural networks with time delay

doi:10.1088/1674-1056/ac7209

中国物理B ›› 2022, Vol. 31 ›› Issue (10): 100201-100201.doi: 10.1088/1674-1056/ac7209

• GENERAL • 下一篇

Finite-time Mittag—Leffler synchronization of fractional-order complex-valued memristive neural networks with time delay

Guan Wang(王冠), Zhixia Ding(丁芝侠)^†, Sai Li(李赛), Le Yang(杨乐), and Rui Jiao(焦睿)

Hubei Key Laboratory of Optical Information and Pattern Recognition, School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China

收稿日期:2022-03-15 修回日期:2022-04-22 出版日期:2022-10-16 发布日期:2022-09-16
通讯作者: Zhixia Ding E-mail:zxding89@163.com
基金资助:
Project supported by the National Natural Science Foundation of China (Grant Nos. 62176189 and 62106181) and the Hubei Province Key Laboratory of Systems Science in Metallurgical Process (Wuhan University of Science and Technology) (Grant No. Y202002).

Finite-time Mittag—Leffler synchronization of fractional-order complex-valued memristive neural networks with time delay

Guan Wang(王冠), Zhixia Ding(丁芝侠)^†, Sai Li(李赛), Le Yang(杨乐), and Rui Jiao(焦睿)

Hubei Key Laboratory of Optical Information and Pattern Recognition, School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China

Received:2022-03-15 Revised:2022-04-22 Online:2022-10-16 Published:2022-09-16
Contact: Zhixia Ding E-mail:zxding89@163.com
Supported by:
Project supported by the National Natural Science Foundation of China (Grant Nos. 62176189 and 62106181) and the Hubei Province Key Laboratory of Systems Science in Metallurgical Process (Wuhan University of Science and Technology) (Grant No. Y202002).

摘要/Abstract

摘要： Without dividing the complex-valued systems into two real-valued ones, a class of fractional-order complex-valued memristive neural networks (FCVMNNs) with time delay is investigated. Firstly, based on the complex-valued sign function, a novel complex-valued feedback controller is devised to research such systems. Under the framework of Filippov solution, differential inclusion theory and Lyapunov stability theorem, the finite-time Mittag—Leffler synchronization (FTMLS) of FCVMNNs with time delay can be realized. Meanwhile, the upper bound of the synchronization settling time (SST) is less conservative than previous results. In addition, by adjusting controller parameters, the global asymptotic synchronization of FCVMNNs with time delay can also be realized, which improves and enrich some existing results. Lastly, some simulation examples are designed to verify the validity of conclusions.

关键词: finite-time Mittag—Leffler synchronization, fractional-order complex-valued memristive neural networks, time delay

Abstract: Without dividing the complex-valued systems into two real-valued ones, a class of fractional-order complex-valued memristive neural networks (FCVMNNs) with time delay is investigated. Firstly, based on the complex-valued sign function, a novel complex-valued feedback controller is devised to research such systems. Under the framework of Filippov solution, differential inclusion theory and Lyapunov stability theorem, the finite-time Mittag—Leffler synchronization (FTMLS) of FCVMNNs with time delay can be realized. Meanwhile, the upper bound of the synchronization settling time (SST) is less conservative than previous results. In addition, by adjusting controller parameters, the global asymptotic synchronization of FCVMNNs with time delay can also be realized, which improves and enrich some existing results. Lastly, some simulation examples are designed to verify the validity of conclusions.

Key words: finite-time Mittag—Leffler synchronization, fractional-order complex-valued memristive neural networks, time delay

中图分类号: (Control theory)

02.30.Yy

Guan Wang(王冠), Zhixia Ding(丁芝侠), Sai Li(李赛), Le Yang(杨乐), and Rui Jiao(焦睿). Finite-time Mittag—Leffler synchronization of fractional-order complex-valued memristive neural networks with time delay[J]. 中国物理B, 2022, 31(10): 100201-100201.

参考文献

[1] Eberhart R and Micheli-Tzanakou E 1990 IEEE Trans. Neural Netw. Learn. Syst. 1 305
[2] Chen W, Ding D, Dong H and Wei G 2019 IEEE Trans. Syst. Man Cybern. 49 1688
[3] Dong T and Huang T W 2019 IEEE Trans. Neural Netw. Learn. Syst. 31 4999
[4] Li C, Belkin D, Li Y, et al. 2018 Nat. Commun. 9 2385
[5] Che H J and Wang J 2018 IEEE Trans. Neural Netw. Learn. Syst. 30 2503
[6] Chen C, Ding Z X, Li S and Wang L H 2020 Chin. Phys. B 29 040202
[7] Ding Z X, Chen C, Wen S P, Li S and Wang L H 2022 Neurocomputing 469 138
[8] Chen J J, Chen B S and Zeng Z G 2018 Neural Netw. 100 10
[9] Zhou B and Song Q K 2013 IEEE Trans. Neural Netw. Learn. Syst. 24 1227
[10] Jankowski S, Lozowski A and Zurada J M 1996 IEEE Trans. Neural Netw. Learn. Syst. 7 1491
[11] Lee D L 2001 IEEE Trans. Neural Netw. 12 1260
[12] Yu J, Hu C, Jiang H J and Wang L M 2020 Neural Netw. 124 50
[13] Wang X H, Wang Z, Song Q K,Shen H and Huang X 2020 Neural Netw. 121 329
[14] Zhang Y L and Deng S F. 2020 Neural Process. Lett. 52 1263
[15] Zhang Z Y, Liu X P, Zhou D H, Lin C, Chen J and Wang H X 2017 IEEE Trans. Syst. Man Cybern. 48 2371
[16] Hirose A 2012 Springer Science & Business Media 131 2
[17] Aizenberg I and Moraga C 2007 Soft Comput.11 169
[18] Aizenberg I 2017 Multiple-Valued Logic and Complex-Valued Neural Networks. In: Seising R and Allende-Cid H (eds) Claudio Moraga: A Passion for Multi-Valued Logic and Soft Computing. Studies in Fuzziness and Soft Computing, (Berlin: Springer) vol. 349, pp. 153—171
[19] Cha I and Kassam S A 1995 IEEE J. Sel. Areas Commun. 13 122
[20] Chen S, Hanzo L and Tan S 2018 IEEE Trans. Neural Netw. 19 1659
[21] Ding Z X, Zeng Z G and Wang L M 2017 IEEE Trans. Neural Netw. Learn. Syst. 29 1477
[22] Ding Z X, Zeng Z G, Zhang H, Wang L M and Wang L H 2019 Neurocomputing 351 51
[23] Chen L P, Huang T W, Tenreiro Machado J A, Lopes Antonio M, Chai Y and Wu R C 2019 Neural Netw. 118 289
[24] Zhang F H and Zeng Z G 2020 IEEE Trans. Neural Netw. Learn. Syst. 32 177
[25] Lundstrom Brian N, Higgs Matthew H, Spain William J and Fairhall Adrienne L 2008 Nat. Neurosci. 11 1335
[26] Rakkiyappan R, Cao J D and Velmurugan G 2014 IEEE Trans. Neural Netw. Learn. Syst. 26 84
[27] You X X, Song Q K and Zhao Z J 2020 Neural Netw. 122 382
[28] Rakkiyappan R, Velmurugan G and Cao J D 2015 Chaos, Solitons & Fractals 78 297
[29] Bao H B, Park J H and Cao J D 2016 Neural Netw. 81 16
[30] Li H L, Hu C, Cao J D, Jiang H J and Ahmed A 2019 Neural Netw. 118 102
[31] Yang S, Yu J, Hu C and Jiang H J 2018 Neural Netw. 104 104
[32] Zhang L, Song Q K and Zhao Z J 2017 Appl. Math. Comput. 298 296
[33] Zheng B B, Hu C, Yu J and Jiang H J 2020 Neurocomputing 373 70
[34] Ding Z X, Zhang H, Zeng Z G, Yang L and Li S 2021 IEEE Trans. Neural Netw. Learn. Syst.
[35] Chua L 1971 IEEE Trans. Circuit Syst. I. Reg. Papers 18 507
[36] Yu F, Zhang Z N, Shen H, Huang Y Y, Cai S and Du S C 2022 Chin. Phys. B 31 020505
[37] Yao W, Wang C H, Sun Y C, Zhou C and Lin H R 2020 Neurocomputing 404 367
[38] Gupta I, Serb A, Khiat A, Zeitler R, Vassanelli S and Prodromakis T 2016 Nat. Commun. 7 1
[39] Cai F X, Kumar S, Van Vaerenbergh T, et al. 2020 Nat. Electron. 3 409
[40] Wei H Z, Li R X, Chen C R and Tu Z W 2017 Neural Process. Lett. 45 379
[41] Zhang W W, Zhang H, Cao J D, Alsaadi Fuad E and Chen D Y 2019 Neural Netw. 110 186
[42] Chang W T, Zhu S, Li J Y and Sun K L 2018 Appl. Math. Comput. 338 346
[43] Zhang Y L and Deng S F 2019 Chaos, Solitons & Fractals 128 176
[44] Chen J J, Chen B S and Zeng Z G 2018 IEEE Trans. Syst. Man Cybern. 49 2519
[45] Syed Ali M, Narayanan G, Orman Z, Shekher V and Arik S 2020 Neural Process. Lett. 51 407
[46] Liu G B, Xu S Y, Wei Y L, Qi Z D and Zhang Z Q 2018 Appl. Math. Comput. 320 769
[47] Pecora L M and Carroll T L 1990 Phys. Rev. Lett. 64 821
[48] Wu G C, Deng Z G, Baleanu D and Zeng D Q 2019 Chaos 29 083103
[49] Borisyuk R, Borisyuk G and Kazanovich Y 1998 Behav. Brain Sci. 21 833
[50] Psaltis D, Sideris A and Yamamura A A 1988 IEEE Contr. Syst. Mag. 8 17
[51] Ding D W, Yao X L and Zhang H W 2020 Neural Process. Lett. 51 325
[52] Zhang S, Yang Y, Li L and Wu D H 2021 Neural Process. Lett. 53 865
[53] Mohammadpour S and Binazadeh T 2018 Syst. Sci. Control. Eng. 6 28
[54] Li Y, Chen Y Q and Podlubny I 2009 Automatica 45 1965
[55] Gu Y J, Yu Y G and Wang H 2016 J Franklin Inst. 353 3657
[56] Filippov A F 2013 Differential Equations with Discontinuous Righthand Sides - Control Systems . In: Arscott F M (ed) Mathematics and its Applications (Netherlands: Springer) vol. 18, p. 48
[57] Hu C, Yu J and Jiang H J 2014 Neurocomputing 143 90

Finite-time Mittag—Leffler synchronization of fractional-order complex-valued memristive neural networks with time delay

Finite-time Mittag—Leffler synchronization of fractional-order complex-valued memristive neural networks with time delay

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	Zhan-Hong Guo(郭展宏), Zhi-Jun Li(李志军), Meng-Jiao Wang(王梦蛟), and Ming-Lin Ma(马铭磷). Hopf bifurcation and phase synchronization in memristor-coupled Hindmarsh-Rose and FitzHugh-Nagumo neurons with two time delays[J]. 中国物理B, 2023, 32(3): 38701-038701.
[2]	Xuan Ma(马璇), Yaya Zhao(赵鸭鸭), Yafeng Wang(王亚峰), Yueling Chen(陈月玲), and Hengtong Wang(王恒通). Effect of autaptic delay signal on spike-timing precision of single neuron[J]. 中国物理B, 2023, 32(3): 38703-038703.
[3]	Xun-Qin Huo(火勋琴), Wei-Feng Yang(杨玮枫), Wen-Hui Dong(董文卉), Fa-Cheng Jin(金发成), Xi-Wang Liu(刘希望), Hong-Dan Zhang(张宏丹), and Xiao-Hong Song(宋晓红). Review on typical applications and computational optimizations based on semiclassical methods in strong-field physics[J]. 中国物理B, 2022, 31(3): 33101-033101.
[4]	Changbao Deng(邓长宝), Weinuo Jiang(蒋未诺), and Shihong Wang(王世红). Inferring interactions of time-delayed dynamic networks by random state variable resetting[J]. 中国物理B, 2022, 31(3): 30502-030502.
[5]	Wenjie Yang(杨文杰). Bifurcation and dynamics in double-delayed Chua circuits with periodic perturbation[J]. 中国物理B, 2022, 31(2): 20201-020201.
[6]	Ben Cao(曹奔), Huaguang Gu(古华光), and Yuye Li(李玉叶). Delayed excitatory self-feedback-induced negative responses of complex neuronal bursting patterns[J]. 中国物理B, 2021, 30(5): 50502-050502.
[7]	Guoyuan Qi(齐国元) and Zimou Wang(王子谋). Modeling and dynamics of double Hindmarsh-Rose neuron with memristor-based magnetic coupling and time delay[J]. 中国物理B, 2021, 30(12): 120516-120516.
[8]	Weilin Ren(任卫林), Rongjun Cheng(程荣军), and Hongxia Ge(葛红霞). Stabilization strategy of a car-following model with multiple time delays of the drivers[J]. 中国物理B, 2021, 30(12): 120506-120506.
[9]	黄玉娇, 袁孝焰, 杨旭华, 龙海霞, 肖杰. Multiple Lagrange stability and Lyapunov asymptotical stability of delayed fractional-order Cohen-Grossberg neural networks[J]. 中国物理B, 2020, 29(2): 20703-020703.
[10]	魏长立, 赵曦. Validity of extracting photoionization time delay from the first moment of streaking spectrogram[J]. 中国物理B, 2019, 28(1): 13201-013201.
[11]	钱郁, 高红艳, 姚成贵, 崔晓华, 马军. Synchronization performance in time-delayed random networks induced by diversity in system parameter[J]. 中国物理B, 2018, 27(10): 108902-108902.
[12]	杨增强, 叶地发, 傅立斌. Attosecond transient absorption spectroscopy: Comparative study based on three-level modeling[J]. 中国物理B, 2018, 27(1): 13301-013301.
[13]	王莉芳, 仇康, 贾亚. Effects of time delays in a mathematical bone model[J]. 中国物理B, 2017, 26(3): 30503-030503.
[14]	王雪峰, 王元庆, 苏金善, 杨兴雨. Locating the position of objects in non-line-of-sight based on time delay estimation[J]. 中国物理B, 2016, 25(8): 84203-084203.
[15]	冷卉, 吴召艳. Cluster synchronization of community network with distributed time delays via impulsive control[J]. 中国物理B, 2016, 25(11): 110501-110501.