Non-Fragile H∞ Synchronization for Markov Jump Singularly Perturbed Coupled Neural Networks Subject to Double-Layer Switching Regulation
This work explores the <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a nove...
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Published in | IEEE transaction on neural networks and learning systems Vol. 34; no. 5; pp. 2682 - 2692 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
United States
IEEE
01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
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Abstract | This work explores the <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation. |
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AbstractList | This work explores the [Formula Omitted] synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the [Formula Omitted] performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation. This work explores the H synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the H performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation. This work explores the <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation. This work explores the H∞ synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the H∞ performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation.This work explores the H∞ synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the H∞ performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation. |
Author | Cao, Jinde Hu, Xiaohui Shen, Hao Qian, Wenhua Wang, Jing |
Author_xml | – sequence: 1 givenname: Hao orcidid: 0000-0001-7024-6573 surname: Shen fullname: Shen, Hao email: haoshen10@gmail.com organization: Anhui Province Key Laboratory of Special Heavy Load Robot, Anhui University of Technology, Ma'anshan, China – sequence: 2 givenname: Xiaohui surname: Hu fullname: Hu, Xiaohui email: hxh19960726@gmail.com organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China – sequence: 3 givenname: Jing orcidid: 0000-0002-5519-9016 surname: Wang fullname: Wang, Jing email: jingwang08@126.com organization: School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan, China – sequence: 4 givenname: Jinde orcidid: 0000-0003-3133-7119 surname: Cao fullname: Cao, Jinde email: jdcao@seu.edu.cn organization: School of Mathematics, Southeast University, Nanjing, China – sequence: 5 givenname: Wenhua surname: Qian fullname: Qian, Wenhua email: whqian@ynu.edu.cn organization: School of Computer Science and Engineering, Yunnan University, Kunming, China |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34487505$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.1088/0951-7715/22/4/011 10.1109/TNNLS.2018.2839020 10.1109/TSMC.2017.2758381 10.1109/9.151120 10.1109/TSMC.2018.2834823 10.1016/j.amc.2021.126087 10.1007/s11424-020-0106-9 10.1109/TFUZZ.2010.2047648 10.1016/j.automatica.2015.02.010 10.1109/TCYB.2013.2283308 10.1007/s11071-013-0949-x 10.1016/j.automatica.2013.12.028 10.1007/978-3-319-45405-4 10.1109/TNNLS.2014.2387443 10.1109/TNNLS.2020.2995708 10.1109/TSMC.2018.2876203 10.1109/TNNLS.2013.2271357 10.1038/990101 10.1016/j.neucom.2019.02.023 10.1109/TNNLS.2011.2177671 10.1109/TCSI.2020.3022729 10.1109/TFUZZ.2019.2930032 10.1016/S0021-8928(61)80009-9 10.1016/j.isatra.2020.11.029 10.1109/TAC.2004.825641 10.1109/CDC.1999.831330 10.1016/j.fss.2019.09.001 10.1016/j.neunet.2004.02.001 10.1007/s11071-020-05786-1 10.1109/TNN.2004.841813 10.1002/rnc.2869 10.1016/j.automatica.2019.108711 10.1142/4703 10.1109/TCSII.2019.2941546 |
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References | ref13 ref35 ref34 ref15 ref37 ref36 ref31 ref11 ref33 ref10 shen (ref12) 2020 shen (ref22) 2020 ref2 ref1 ref17 ref39 ref16 ref19 ref18 chung (ref30) 1997 li (ref38) 2020; 50 ref24 ref23 ref26 ref25 ref20 ref21 ref28 ref27 ref29 ref8 zhang (ref32) 2009 ref7 ref9 ref4 ref3 ref6 ref5 ref40 wang (ref14) 2020 |
References_xml | – ident: ref2 doi: 10.1088/0951-7715/22/4/011 – ident: ref31 doi: 10.1109/TNNLS.2018.2839020 – ident: ref36 doi: 10.1109/TSMC.2017.2758381 – ident: ref40 doi: 10.1109/9.151120 – ident: ref18 doi: 10.1109/TSMC.2018.2834823 – ident: ref15 doi: 10.1016/j.amc.2021.126087 – year: 1997 ident: ref30 publication-title: Spectral Graph Theory – ident: ref26 doi: 10.1007/s11424-020-0106-9 – year: 2020 ident: ref12 article-title: Fault-tolerant fuzzy control for semi-Markov jump nonlinear systems subject to incomplete SMK and actuator failures publication-title: IEEE Trans Fuzzy Syst – ident: ref37 doi: 10.1109/TFUZZ.2010.2047648 – start-page: 197 year: 2009 ident: ref32 article-title: $H_\infty$ control of a class of piecewise homogeneous Markov jump linear systems publication-title: Proc 7th Asian Control Conf – ident: ref29 doi: 10.1016/j.automatica.2015.02.010 – ident: ref6 doi: 10.1109/TCYB.2013.2283308 – ident: ref21 doi: 10.1007/s11071-013-0949-x – ident: ref34 doi: 10.1016/j.automatica.2013.12.028 – ident: ref23 doi: 10.1007/978-3-319-45405-4 – ident: ref17 doi: 10.1109/TNNLS.2014.2387443 – ident: ref28 doi: 10.1109/TNNLS.2020.2995708 – ident: ref39 doi: 10.1109/TSMC.2018.2876203 – ident: ref4 doi: 10.1109/TNNLS.2013.2271357 – ident: ref10 doi: 10.1038/990101 – ident: ref19 doi: 10.1016/j.neucom.2019.02.023 – ident: ref16 doi: 10.1109/TNNLS.2011.2177671 – ident: ref24 doi: 10.1109/TCSI.2020.3022729 – year: 2020 ident: ref14 article-title: $H_\infty$ synchronization for fuzzy Markov jump chaotic systems with piecewise-constant transition probabilities subject to PDT switching rule publication-title: IEEE Trans Fuzzy Syst – ident: ref20 doi: 10.1109/TFUZZ.2019.2930032 – ident: ref27 doi: 10.1016/S0021-8928(61)80009-9 – year: 2020 ident: ref22 article-title: Generalized dissipative state estimation of singularly perturbed switched complex dynamic networks with persistent dwell-time mechanism publication-title: IEEE Trans Syst Man Cybern Syst – ident: ref25 doi: 10.1016/j.isatra.2020.11.029 – ident: ref33 doi: 10.1109/TAC.2004.825641 – ident: ref35 doi: 10.1109/CDC.1999.831330 – ident: ref11 doi: 10.1016/j.fss.2019.09.001 – ident: ref1 doi: 10.1016/j.neunet.2004.02.001 – ident: ref13 doi: 10.1007/s11071-020-05786-1 – ident: ref8 doi: 10.1109/TNN.2004.841813 – ident: ref3 doi: 10.1002/rnc.2869 – ident: ref5 doi: 10.1016/j.automatica.2019.108711 – ident: ref7 doi: 10.1142/4703 – volume: 50 start-page: 2860 year: 2020 ident: ref38 article-title: Resilient asynchronous $H_\infty$ control for discrete-time Markov jump singularly perturbed systems based on hidden Markov model publication-title: IEEE Trans Syst Man Cybern Syst – ident: ref9 doi: 10.1109/TCSII.2019.2941546 |
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Snippet | This work explores the <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> synchronization issue for singularly perturbed... This work explores the H synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain... This work explores the [Formula Omitted] synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints... This work explores the H∞ synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain... |
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SubjectTerms | Control systems Control systems design Controllers Convexity Double-layer switching regulation Dwell time Frequency modulation H-infinity control Markov analysis Markov chains Markov jump neural networks Markov processes Mathematical models Neural networks non-fragile synchronization Optimization Regulation singularly perturbed coupled neural networks (SPCNNs) Stochasticity Switches Switching Synchronism Synchronization Transition probabilities |
Title | Non-Fragile H∞ Synchronization for Markov Jump Singularly Perturbed Coupled Neural Networks Subject to Double-Layer Switching Regulation |
URI | https://ieeexplore.ieee.org/document/9530248 https://www.ncbi.nlm.nih.gov/pubmed/34487505 https://www.proquest.com/docview/2808836163 https://www.proquest.com/docview/2570113123 |
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