H∞ synchronization of Markov jump neural networks with MPDT switching transition probabilities under DoS attacks
In this paper, the H∞$$ {H}_{\infty } $$ synchronization issue of discrete‐time Markov jump neural networks under DoS attacks is studied, in which the transition probability of the Markov chain is considered as time‐varying governed by a mode‐dependent persistent dwell‐time switching rule. The purpo...
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| Published in | Mathematical methods in the applied sciences Vol. 46; no. 14; pp. 15412 - 15425 |
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| Abstract | In this paper, the
H∞$$ {H}_{\infty } $$ synchronization issue of discrete‐time Markov jump neural networks under DoS attacks is studied, in which the transition probability of the Markov chain is considered as time‐varying governed by a mode‐dependent persistent dwell‐time switching rule. The purpose of this paper is to design a suitable controller such that mean‐square exponentially stable of the synchronization error system can be accomplished, and an
H∞$$ {H}_{\infty } $$ performance is satisfied under denial of service (DoS) attacks. In order to reduce the conservatism of obtained results, some synchronization analysis criteria are obtained by adopting an activation function division method. Based on these criteria, an effective security synchronization controller design method is presented. Conclusively, an example is given to verify the validity of the results. |
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| AbstractList | In this paper, the
synchronization issue of discrete‐time Markov jump neural networks under DoS attacks is studied, in which the transition probability of the Markov chain is considered as time‐varying governed by a mode‐dependent persistent dwell‐time switching rule. The purpose of this paper is to design a suitable controller such that mean‐square exponentially stable of the synchronization error system can be accomplished, and an
performance is satisfied under denial of service (DoS) attacks. In order to reduce the conservatism of obtained results, some synchronization analysis criteria are obtained by adopting an activation function division method. Based on these criteria, an effective security synchronization controller design method is presented. Conclusively, an example is given to verify the validity of the results. In this paper, the H∞$$ {H}_{\infty } $$ synchronization issue of discrete‐time Markov jump neural networks under DoS attacks is studied, in which the transition probability of the Markov chain is considered as time‐varying governed by a mode‐dependent persistent dwell‐time switching rule. The purpose of this paper is to design a suitable controller such that mean‐square exponentially stable of the synchronization error system can be accomplished, and an H∞$$ {H}_{\infty } $$ performance is satisfied under denial of service (DoS) attacks. In order to reduce the conservatism of obtained results, some synchronization analysis criteria are obtained by adopting an activation function division method. Based on these criteria, an effective security synchronization controller design method is presented. Conclusively, an example is given to verify the validity of the results. In this paper, the H∞$$ {H}_{\infty } $$ synchronization issue of discrete‐time Markov jump neural networks under DoS attacks is studied, in which the transition probability of the Markov chain is considered as time‐varying governed by a mode‐dependent persistent dwell‐time switching rule. The purpose of this paper is to design a suitable controller such that mean‐square exponentially stable of the synchronization error system can be accomplished, and an H∞$$ {H}_{\infty } $$ performance is satisfied under denial of service (DoS) attacks. In order to reduce the conservatism of obtained results, some synchronization analysis criteria are obtained by adopting an activation function division method. Based on these criteria, an effective security synchronization controller design method is presented. Conclusively, an example is given to verify the validity of the results. |
| Author | Li, Feng Wang, Xiaomin Shen, Hao Wang, Jing |
| Author_xml | – sequence: 1 givenname: Xiaomin surname: Wang fullname: Wang, Xiaomin organization: Anhui University of Technology – sequence: 2 givenname: Feng orcidid: 0000-0002-1711-3891 surname: Li fullname: Li, Feng email: fengli4131@gmail.com organization: Anhui University of Technology – sequence: 3 givenname: Hao surname: Shen fullname: Shen, Hao email: haoshen10@gmail.com organization: Anhui University of Technology – sequence: 4 givenname: Jing surname: Wang fullname: Wang, Jing organization: Anhui University of Technology |
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| References | 2018; 29 2021; 44 2023; 34 2017; 2017 2013; 43 2017; 28 2010; 18 2020; 60 2018; 444 2015; 54 2020; 107 2018; 41 2020; 12 2022; 65 2020; 509 2019; 360 2021; 51 2012; 349 2022; 164 2018; 2018 2023; 46 2013; 36 2010; 46 2019; 42 2022 2018; 355 2021; 358 2020; 357 2019; 49 2019; 356 2020; 25 2022; 52 2022; 30 2022; 32 2022; 33 2013 2021; 40 2019; 331 2014; 77 e_1_2_8_28_1 e_1_2_8_29_1 e_1_2_8_24_1 e_1_2_8_25_1 e_1_2_8_26_1 Maharajan C. (e_1_2_8_12_1) 2018; 2018 Manickam I. (e_1_2_8_31_1) 2020; 25 e_1_2_8_3_1 e_1_2_8_2_1 e_1_2_8_5_1 e_1_2_8_4_1 e_1_2_8_7_1 e_1_2_8_6_1 e_1_2_8_9_1 e_1_2_8_8_1 e_1_2_8_20_1 e_1_2_8_21_1 e_1_2_8_22_1 e_1_2_8_23_1 e_1_2_8_41_1 e_1_2_8_40_1 e_1_2_8_17_1 e_1_2_8_18_1 e_1_2_8_39_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_15_1 e_1_2_8_38_1 e_1_2_8_16_1 e_1_2_8_37_1 e_1_2_8_32_1 e_1_2_8_10_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_33_1 Wu J. (e_1_2_8_27_1) 2022 e_1_2_8_30_1 |
| References_xml | – volume: 46 start-page: 3783 issue: 4 year: 2023 end-page: 3796 article-title: static output feedback control for singularly perturbed persistent dwell‐time switched systems with time‐varying delay and deception attacks publication-title: Math. Methods Appl. Sci. – volume: 164 start-page: 112724 year: 2022 article-title: Finite‐time optimal feedback control mechanism for knowledge transmission in complex networks via model predictive control publication-title: Chaos Solitons Fractals – volume: 30 start-page: 1889 issue: 6 year: 2022 end-page: 1899 article-title: Observer‐based sliding mode control for networked fuzzy singularly perturbed systems under weighted try‐once‐discard protocol publication-title: IEEE Trans. Fuzzy Syst. – volume: 36 start-page: 395 issue: 4 year: 2013 end-page: 412 article-title: Delay decomposition approach to state estimation of neural networks with mixed time‐varying delays and Markovian jumping parameters publication-title: Math. Methods Appl. Sci. – volume: 355 start-page: 2735 issue: 5 year: 2018 end-page: 2761 article-title: A state estimation issue for discrete‐time stochastic impulsive genetic regulatory networks in the presence of leakage, multiple delays and Markovian jumping parameters publication-title: J. Franklin Inst. – volume: 42 start-page: 982 issue: 3 year: 2019 end-page: 998 article-title: Robust partially mode‐dependent filtering for discrete‐time nonhomogeneous Markovian jump neural networks with additive gain perturbations publication-title: Math. Methods Appl. Sci. – volume: 29 start-page: 483 year: 2018 end-page: 492 article-title: Exponential stability of semi‐Markovian jump generalized neural networks with interval time‐varying delays publication-title: Neural Comput. Appl. – start-page: 1854 year: 2013 end-page: 1859 – volume: 52 start-page: 481 issue: 1 year: 2022 end-page: 494 article-title: Guaranteed cost finite‐time control of uncertain coupled neural networks publication-title: IEEE Trans. Cybern. – volume: 331 start-page: 403 year: 2019 end-page: 411 article-title: Event‐triggered passive synchronization for Markov jump neural networks subject to randomly occurring gain variations publication-title: Neurocomputing – volume: 357 start-page: 10329 issue: 15 year: 2020 end-page: 10352 article-title: Quasi‐time‐dependent robust static output feedback control for uncertain discrete‐time switched systems with mode‐dependent persistent dwell‐time publication-title: J. Franklin Inst. – volume: 107 start-page: 307 year: 2020 end-page: 315 article-title: Simultaneous estimation of state and packet‐loss occurrences in networked control systems publication-title: ISA Trans. – volume: 444 start-page: 122 year: 2018 end-page: 134 article-title: A cooperative detection and compensation mechanism against denial‐of‐service attack for cyber‐physical systems publication-title: Inf. Sci. – volume: 356 start-page: 561 issue: 1 year: 2019 end-page: 591 article-title: Further mean‐square asymptotic stability of impulsive discrete‐time stochastic BAM neural networks with Markovian jumping and multiple time‐varying delays publication-title: J. Franklin Inst. – volume: 25 start-page: 726 issue: 5 year: 2020 end-page: 744 article-title: Novel lagrange sense exponential stability criteria for time‐delayed stochastic Cohen–Grossberg neural networks with Markovian jump parameters: a graph‐theoretic approach publication-title: Nonlinear Anal. Model. Control – volume: 358 start-page: 2895 issue: 6 year: 2021 end-page: 2914 article-title: Event‐triggered control for network‐based uncertain Markov jump systems under Dos attacks publication-title: J. Franklin Inst. – volume: 349 start-page: 1989 issue: 6 year: 2012 end-page: 2003 article-title: control of a class of discrete‐time Markov jump linear systems with piecewise‐constant TPs subject to average dwell time switching publication-title: J. Franklin Inst. – volume: 360 start-page: 1 year: 2019 end-page: 13 article-title: Synchronization control for Markov jump neural networks subject to HMM observation and partially known detection probabilities publication-title: Appl. Math. Comput. – volume: 509 start-page: 304 year: 2020 end-page: 316 article-title: Dynamic event‐triggered mechanism for non‐fragile state estimation of complex networks under randomly occurring sensor saturations publication-title: Inf. Sci. – volume: 33 start-page: 4160 issue: 9 year: 2022 end-page: 4172 article-title: state estimation for BAM neural networks with binary mode switching and distributed leakage delays under periodic scheduling protocol publication-title: IEEE Trans. Neural Netw. Learn. Syst. – start-page: 1 year: 2022 end-page: 20 article-title: Recent advances in event‐triggered security control of networked systems: a survey publication-title: Int. J. Syst. Sci. – volume: 52 start-page: 5152 issue: 8 year: 2022 end-page: 5164 article-title: Proportional‐integral observer design for uncertain time‐delay systems subject to deception attacks: a outlier‐resistant approach publication-title: IEEE Trans. Syst. Man Cybern: Syst. – volume: 44 start-page: 419 issue: 1 year: 2021 end-page: 440 article-title: Stability of discrete‐time fractional‐order time‐delayed neural networks in complex field publication-title: Math. Methods Appl. Sci. – volume: 54 start-page: 201 year: 2015 end-page: 209 article-title: Non‐weighted quasi‐time‐dependent filtering for switched linear systems with persistent dwell‐time publication-title: Automatica – volume: 65 start-page: 199204 issue: 9 year: 2022 article-title: Observer‐based ‐ control for singularly perturbed semi‐Markov jump systems with improved weighted TOD protocol publication-title: Sci. China Inf. Sci. – volume: 40 start-page: 5997 issue: 12 year: 2021 end-page: 6015 article-title: Passivity and reduced‐order feedback passification of discrete‐time switched systems with mode‐dependent persistent dwell time publication-title: Circuits, Syst. Signal Process. – volume: 12 start-page: 1035 issue: 6 year: 2020 article-title: An extended analysis on robust dissipativity of uncertain stochastic generalized neural networks with Markovian jumping parameters publication-title: Symmetry – volume: 77 start-page: 1709 issue: 4 year: 2014 end-page: 1720 article-title: Finite‐time synchronization control for uncertain Markov jump neural networks with input constraints publication-title: Nonlinear Dyn. – volume: 46 start-page: 1081 issue: 6 year: 2010 end-page: 1088 article-title: Markov jump linear systems with switching transition rates: mean square stability with dwell‐time publication-title: Automatica – volume: 2018 start-page: 1 issue: 1 year: 2018 end-page: 31 article-title: Global exponential stability of Markovian jumping stochastic impulsive uncertain BAM neural networks with leakage, mixed time delays, and ‐inverse Hölder activation functions publication-title: Adv. Differ. Equa. – volume: 60 start-page: 80 year: 2020 end-page: 86 article-title: Extended Kalman filtering subject to random transmission delays: dealing with packet disorders publication-title: Inf. Fusion – volume: 51 start-page: 3926 issue: 6 year: 2021 end-page: 3938 article-title: Observer‐based PID security control for discrete time‐delay systems under cyber‐attacks publication-title: IEEE Trans. Syst. Man, Cybern. Syst. – volume: 41 start-page: 6968 issue: 16 year: 2018 end-page: 6983 article-title: State‐dependent switching control of delayed switched systems with stable and unstable modes publication-title: Math. Methods Appl. Sci. – volume: 32 start-page: 2760 issue: 5 year: 2022 end-page: 2779 article-title: Neural‐network‐based control for discrete‐time nonlinear systems with denial‐of‐service attack: the adaptive event‐triggered case publication-title: Int. J. Robust Nonlinear Control – volume: 34 start-page: 2682 issue: 5 year: 2023 end-page: 2692 article-title: Non‐fragile synchronization for Markov jump singularly perturbed coupled neural networks subject to double‐layer switching regulation publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 2017 start-page: 1 year: 2017 end-page: 28 article-title: Enhanced robust finite‐time passivity for Markovian jumping discrete‐time BAM neural networks with leakage delay publication-title: Adv. Diff. Equ. – volume: 43 start-page: 1796 issue: 6 year: 2013 end-page: 1806 article-title: Stochastic synchronization of Markovian jump neural networks with time‐varying delay using sampled data publication-title: IEEE Trans. Cybern. – volume: 28 start-page: 740 issue: 3 year: 2017 end-page: 752 article-title: Sampled‐data synchronization analysis of Markovian neural networks with generally incomplete transition rates publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 49 start-page: 2026 issue: 10 year: 2019 end-page: 2035 article-title: Event‐triggered filtering for discrete‐time T–S fuzzy systems via network delay optimization technique publication-title: IEEE Trans. Syst. Man, Cybern. Syst. – volume: 18 start-page: 712 issue: 4 year: 2010 end-page: 725 article-title: Robust fuzzy output‐feedback control with multiple probabilistic delays and multiple missing measurements publication-title: IEEE Trans. Fuzzy Syst. – ident: e_1_2_8_14_1 doi: 10.1002/mma.2597 – ident: e_1_2_8_15_1 doi: 10.1007/s11432-021-3345-1 – ident: e_1_2_8_16_1 doi: 10.1186/s13662-017-1378-9 – ident: e_1_2_8_19_1 doi: 10.1016/j.jfranklin.2017.12.036 – ident: e_1_2_8_5_1 doi: 10.1016/j.chaos.2022.112724 – ident: e_1_2_8_8_1 doi: 10.1016/j.neucom.2018.11.011 – ident: e_1_2_8_26_1 doi: 10.1016/j.jfranklin.2020.08.010 – ident: e_1_2_8_18_1 doi: 10.1109/TFUZZ.2021.3070125 – ident: e_1_2_8_37_1 doi: 10.1016/j.jfranklin.2021.01.026 – ident: e_1_2_8_6_1 doi: 10.1016/j.jfranklin.2018.09.037 – ident: e_1_2_8_41_1 doi: 10.1016/j.amc.2019.04.032 – ident: e_1_2_8_24_1 doi: 10.1002/mma.5209 – ident: e_1_2_8_32_1 doi: 10.1016/j.isatra.2020.08.006 – ident: e_1_2_8_3_1 doi: 10.1109/TNNLS.2021.3055942 – ident: e_1_2_8_7_1 doi: 10.1109/TCYB.2020.2971265 – ident: e_1_2_8_22_1 doi: 10.1109/TNNLS.2021.3107607 – ident: e_1_2_8_2_1 doi: 10.1016/j.ins.2019.08.063 – volume: 2018 start-page: 1 issue: 1 year: 2018 ident: e_1_2_8_12_1 article-title: Global exponential stability of Markovian jumping stochastic impulsive uncertain BAM neural networks with leakage, mixed time delays, and α$$ \alpha $$‐inverse Hölder activation functions publication-title: Adv. Differ. Equa. – ident: e_1_2_8_11_1 doi: 10.1007/s00521-016-2461-y – ident: e_1_2_8_34_1 doi: 10.1002/rnc.5831 – ident: e_1_2_8_35_1 – ident: e_1_2_8_40_1 doi: 10.1109/TFUZZ.2010.2047648 – ident: e_1_2_8_20_1 doi: 10.1016/j.automatica.2010.03.007 – ident: e_1_2_8_4_1 doi: 10.1002/mma.6745 – ident: e_1_2_8_23_1 doi: 10.1002/mma.8721 – start-page: 1 year: 2022 ident: e_1_2_8_27_1 article-title: Recent advances in event‐triggered security control of networked systems: a survey publication-title: Int. J. Syst. Sci. – ident: e_1_2_8_30_1 doi: 10.3390/sym12061035 – ident: e_1_2_8_10_1 doi: 10.1007/s11071-014-1412-3 – ident: e_1_2_8_13_1 doi: 10.1109/TSMCB.2012.2230441 – ident: e_1_2_8_36_1 doi: 10.1109/TSMC.2021.3117742 – ident: e_1_2_8_17_1 doi: 10.1109/TNNLS.2015.2507790 – ident: e_1_2_8_38_1 doi: 10.1016/j.ins.2018.02.066 – volume: 25 start-page: 726 issue: 5 year: 2020 ident: e_1_2_8_31_1 article-title: Novel lagrange sense exponential stability criteria for time‐delayed stochastic Cohen–Grossberg neural networks with Markovian jump parameters: a graph‐theoretic approach publication-title: Nonlinear Anal. Model. Control – ident: e_1_2_8_25_1 doi: 10.1007/s00034-021-01768-9 – ident: e_1_2_8_29_1 doi: 10.1109/TSMC.2019.2952539 – ident: e_1_2_8_33_1 doi: 10.1109/TSMC.2018.2810835 – ident: e_1_2_8_39_1 doi: 10.1016/j.automatica.2015.02.010 – ident: e_1_2_8_9_1 doi: 10.1002/mma.5408 – ident: e_1_2_8_21_1 doi: 10.1016/j.jfranklin.2012.04.004 – ident: e_1_2_8_28_1 doi: 10.1016/j.inffus.2020.02.006 |
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H∞$$ {H}_{\infty } $$ synchronization issue of discrete‐time Markov jump neural networks under DoS attacks is studied, in which the... In this paper, the synchronization issue of discrete‐time Markov jump neural networks under DoS attacks is studied, in which the transition probability of the... In this paper, the H∞$$ {H}_{\infty } $$ synchronization issue of discrete‐time Markov jump neural networks under DoS attacks is studied, in which the... |
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| SubjectTerms | Control systems design Controllers Criteria Denial of service attacks DoS attacks H-infinity control Markov chains Markov jump neural networks mode‐dependent persistent dwell‐time switching rule Neural networks Switching synchronization control Time synchronization Transition probabilities |
| Title | H∞ synchronization of Markov jump neural networks with MPDT switching transition probabilities under DoS attacks |
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