Finite-time synchronization of stochastic coupled neural networks subject to Markovian switching and input saturation
This paper addresses the problem of finite-time synchronization of stochastic coupled neural networks (SCNNs) subject to Markovian switching, mixed time delay, and actuator saturation. In addition, coupling strengths of the SCNNs are characterized by mutually independent random variables. By utilizi...
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| Published in | Neural networks Vol. 105; pp. 154 - 165 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Ltd
01.09.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0893-6080 1879-2782 1879-2782 |
| DOI | 10.1016/j.neunet.2018.05.004 |
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| Abstract | This paper addresses the problem of finite-time synchronization of stochastic coupled neural networks (SCNNs) subject to Markovian switching, mixed time delay, and actuator saturation. In addition, coupling strengths of the SCNNs are characterized by mutually independent random variables. By utilizing a simple linear transformation, the problem of stochastic finite-time synchronization of SCNNs is converted into a mean-square finite-time stabilization problem of an error system. By choosing a suitable mode dependent switched Lyapunov–Krasovskii functional, a new set of sufficient conditions is derived to guarantee the finite-time stability of the error system. Subsequently, with the help of anti-windup control scheme, the actuator saturation risks could be mitigated. Moreover, the derived conditions help to optimize estimation of the domain of attraction by enlarging the contractively invariant set. Furthermore, simulations are conducted to exhibit the efficiency of proposed control scheme. |
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| AbstractList | This paper addresses the problem of finite-time synchronization of stochastic coupled neural networks (SCNNs) subject to Markovian switching, mixed time delay, and actuator saturation. In addition, coupling strengths of the SCNNs are characterized by mutually independent random variables. By utilizing a simple linear transformation, the problem of stochastic finite-time synchronization of SCNNs is converted into a mean-square finite-time stabilization problem of an error system. By choosing a suitable mode dependent switched Lyapunov-Krasovskii functional, a new set of sufficient conditions is derived to guarantee the finite-time stability of the error system. Subsequently, with the help of anti-windup control scheme, the actuator saturation risks could be mitigated. Moreover, the derived conditions help to optimize estimation of the domain of attraction by enlarging the contractively invariant set. Furthermore, simulations are conducted to exhibit the efficiency of proposed control scheme. This paper addresses the problem of finite-time synchronization of stochastic coupled neural networks (SCNNs) subject to Markovian switching, mixed time delay, and actuator saturation. In addition, coupling strengths of the SCNNs are characterized by mutually independent random variables. By utilizing a simple linear transformation, the problem of stochastic finite-time synchronization of SCNNs is converted into a mean-square finite-time stabilization problem of an error system. By choosing a suitable mode dependent switched Lyapunov-Krasovskii functional, a new set of sufficient conditions is derived to guarantee the finite-time stability of the error system. Subsequently, with the help of anti-windup control scheme, the actuator saturation risks could be mitigated. Moreover, the derived conditions help to optimize estimation of the domain of attraction by enlarging the contractively invariant set. Furthermore, simulations are conducted to exhibit the efficiency of proposed control scheme.This paper addresses the problem of finite-time synchronization of stochastic coupled neural networks (SCNNs) subject to Markovian switching, mixed time delay, and actuator saturation. In addition, coupling strengths of the SCNNs are characterized by mutually independent random variables. By utilizing a simple linear transformation, the problem of stochastic finite-time synchronization of SCNNs is converted into a mean-square finite-time stabilization problem of an error system. By choosing a suitable mode dependent switched Lyapunov-Krasovskii functional, a new set of sufficient conditions is derived to guarantee the finite-time stability of the error system. Subsequently, with the help of anti-windup control scheme, the actuator saturation risks could be mitigated. Moreover, the derived conditions help to optimize estimation of the domain of attraction by enlarging the contractively invariant set. Furthermore, simulations are conducted to exhibit the efficiency of proposed control scheme. |
| Author | Sakthivel, R. Selvaraj, P. Kwon, O.M. |
| Author_xml | – sequence: 1 givenname: P. surname: Selvaraj fullname: Selvaraj, P. organization: School of Electrical Engineering, Chungbuk National University, 1 Chungdao-ro, Cheongju 28644, South Korea – sequence: 2 givenname: R. surname: Sakthivel fullname: Sakthivel, R. email: krsakthivel@buc.edu.in organization: Department of Mathematics, Bharathiar University, Coimbatore 641046, India – sequence: 3 givenname: O.M. orcidid: 0000-0002-4777-7912 surname: Kwon fullname: Kwon, O.M. email: madwind@chungbuk.ac.kr organization: School of Electrical Engineering, Chungbuk National University, 1 Chungdao-ro, Cheongju 28644, South Korea |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29886328$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.apm.2017.03.024 10.1016/j.neucom.2016.09.004 10.1109/TIE.2014.2351379 10.1016/j.amc.2013.02.030 10.1016/j.automatica.2015.03.010 10.1016/j.neucom.2014.03.042 10.1109/TFUZZ.2015.2513085 10.1109/TSMCB.2012.2199751 10.1109/TNNLS.2013.2295966 10.1109/TIP.2011.2159231 10.1016/j.neucom.2014.02.050 10.1016/j.eswa.2011.11.055 10.1109/TNNLS.2011.2182659 10.1109/TNNLS.2016.2546962 10.1016/j.neunet.2016.10.007 10.1016/j.amc.2016.06.018 10.1016/j.neucom.2017.03.017 10.1109/TASE.2015.2414652 10.1016/j.amc.2015.11.060 10.1016/j.cnsns.2012.02.020 10.1016/j.neucom.2015.12.056 10.1016/j.neucom.2016.10.063 10.1049/iet-cta.2016.0845 10.1109/TSMCB.2010.2053354 10.1109/TCYB.2014.2381604 10.1109/TAC.2016.2604682 10.1016/j.automatica.2015.02.016 10.1109/TNNLS.2016.2599199 10.1007/s11071-016-2635-2 10.1109/TSMC.2016.2562511 10.1049/iet-cta.2014.0383 10.1049/iet-cta.2015.0189 10.1109/TNNLS.2015.2475737 10.1016/j.ins.2017.10.007 10.1016/j.ins.2015.05.031 10.1109/TCSI.2012.2215804 10.1007/s11071-017-3782-9 10.1109/TCYB.2014.2312004 10.1109/TSMCB.2012.2230441 |
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| Keywords | Markovian jumping parameters Saturation effect Coupled stochastic neural networks Finite-time synchronization Stochastic coupling strength |
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| References | Li, Shi, Wu (b9) 2017 Pan, Sun, Gao, Jing (b18) 2016; 13 Wu, Su, Wu (b34) 2015; 9 Xu, Wang, Wei (b35) 2017; 227 Liu, Ho, Cao, Xu (b13) 2017; 28 Wang, Teng, Jiang (b28) 2012; 17 Bao, Park, Cao (b2) 2016; 27 Wu, Shi, Su, Chu (b33) 2014; 44 Wang, Qiu, Gao (b27) 2017; 47 Zheng, Cao (b40) 2014; 141 Park, Kwon, Seuret (b21) 2017; 62 Liu, Wang, Liang, Liu (b15) 2013; 43 Park, Kwon, Park, Lee (b19) 2012; 39 Manivannan, Samidurai, Cao, Alsaedi, Alsaadi (b16) 2018; 424 Zhu, Cao (b41) 2011; 41 Yang, Huang (b37) 2017; 249 Wang, Zhang, Yan (b29) 2015 Mathiyalagan, Park, Sakthivel (b17) 2016; 84 Zhu, Cao (b42) 2012; 23 Chen, Shi, Lim (b4) 2017; 28 Wu, Cao, Li, Alsaedi, Alsaadi (b31) 2017; 85 Li, Shi, Wu, Basin, Lim (b10) 2015; 62 Wang, Zhu (b30) 2015; 54 Yang, Zhang, Sun (b38) 2016; 10 Hunt, Irwin, Warwick (b7) 1995 Ahn, Shi, Wu (b1) 2015; 45 Lian, Wang (b12) 2015; 320 Zhang, Gao (b39) 2017; 219 Lee, Park, Kwon, Sakthivel (b8) 2016; 290 Fang, Park (b5) 2013; 219 Wu, Shi, Su, Chu (b32) 2013; 43 Tang, Gao, Lu, Kurths (b26) 2014; 25 Sakthivel, Anbuvithya, Mathiyalagan, Ma, Prakash (b23) 2016; 275 Cai, Huang, Zhang (b3) 2017; 47 Park, Kwon, Park, Lee, Cha (b20) 2015; 55 Selvaraj, Kaviarasan, Sakthivel, Karimi (b25) 2017; 11 Huang, Li, Huang, He (b6) 2014; 139 Samidurai, Manivannan (b24) 2016; 185 Peng, Yue, Han (b22) 2015 Yang, Cao, Lu (b36) 2013; 60 Li, Wang, Shi (b11) 2016; 24 Zineddin, Wang, Liu (b43) 2011; 20 Liu, Ho, Song, Cao (b14) 2017; 90 Wang (10.1016/j.neunet.2018.05.004_b28) 2012; 17 Park (10.1016/j.neunet.2018.05.004_b20) 2015; 55 Lian (10.1016/j.neunet.2018.05.004_b12) 2015; 320 Park (10.1016/j.neunet.2018.05.004_b19) 2012; 39 Wang (10.1016/j.neunet.2018.05.004_b27) 2017; 47 Ahn (10.1016/j.neunet.2018.05.004_b1) 2015; 45 Wu (10.1016/j.neunet.2018.05.004_b32) 2013; 43 Peng (10.1016/j.neunet.2018.05.004_b22) 2015 Wu (10.1016/j.neunet.2018.05.004_b31) 2017; 85 Li (10.1016/j.neunet.2018.05.004_b11) 2016; 24 Zineddin (10.1016/j.neunet.2018.05.004_b43) 2011; 20 Bao (10.1016/j.neunet.2018.05.004_b2) 2016; 27 Li (10.1016/j.neunet.2018.05.004_b9) 2017 Tang (10.1016/j.neunet.2018.05.004_b26) 2014; 25 Yang (10.1016/j.neunet.2018.05.004_b37) 2017; 249 Cai (10.1016/j.neunet.2018.05.004_b3) 2017; 47 Mathiyalagan (10.1016/j.neunet.2018.05.004_b17) 2016; 84 Zhu (10.1016/j.neunet.2018.05.004_b42) 2012; 23 Zhu (10.1016/j.neunet.2018.05.004_b41) 2011; 41 Sakthivel (10.1016/j.neunet.2018.05.004_b23) 2016; 275 Fang (10.1016/j.neunet.2018.05.004_b5) 2013; 219 Park (10.1016/j.neunet.2018.05.004_b21) 2017; 62 Manivannan (10.1016/j.neunet.2018.05.004_b16) 2018; 424 Wang (10.1016/j.neunet.2018.05.004_b29) 2015 Zhang (10.1016/j.neunet.2018.05.004_b39) 2017; 219 Selvaraj (10.1016/j.neunet.2018.05.004_b25) 2017; 11 Wu (10.1016/j.neunet.2018.05.004_b34) 2015; 9 Liu (10.1016/j.neunet.2018.05.004_b14) 2017; 90 Xu (10.1016/j.neunet.2018.05.004_b35) 2017; 227 Yang (10.1016/j.neunet.2018.05.004_b38) 2016; 10 Li (10.1016/j.neunet.2018.05.004_b10) 2015; 62 Pan (10.1016/j.neunet.2018.05.004_b18) 2016; 13 Yang (10.1016/j.neunet.2018.05.004_b36) 2013; 60 Lee (10.1016/j.neunet.2018.05.004_b8) 2016; 290 Wu (10.1016/j.neunet.2018.05.004_b33) 2014; 44 Hunt (10.1016/j.neunet.2018.05.004_b7) 1995 Zheng (10.1016/j.neunet.2018.05.004_b40) 2014; 141 Liu (10.1016/j.neunet.2018.05.004_b13) 2017; 28 Chen (10.1016/j.neunet.2018.05.004_b4) 2017; 28 Liu (10.1016/j.neunet.2018.05.004_b15) 2013; 43 Huang (10.1016/j.neunet.2018.05.004_b6) 2014; 139 Wang (10.1016/j.neunet.2018.05.004_b30) 2015; 54 Samidurai (10.1016/j.neunet.2018.05.004_b24) 2016; 185 |
| References_xml | – volume: 62 start-page: 2916 year: 2017 end-page: 2922 ident: b21 article-title: Weighted consensus protocols design based on network centrality for multi-agent systems with sampled-data publication-title: IEEE Transactions on Automatic Control – volume: 275 start-page: 213 year: 2016 end-page: 228 ident: b23 article-title: Reliable anti-synchronization conditions for BAM memristive neural networks with different memductance functions publication-title: Applied Mathematics and Computation – volume: 60 start-page: 363 year: 2013 end-page: 376 ident: b36 article-title: Synchronization of randomly coupled neural networks with Markovian jumping and time-delay publication-title: IEEE Transactions on Circuits & Systems I: Regular Papers – volume: 41 start-page: 341 year: 2011 end-page: 353 ident: b41 article-title: Exponential stability of stochastic neural networks with both Markovian jump parameters and mixed time delays publication-title: IEEE Transactions on Systems, Man and Cybernetics: Part B – volume: 424 start-page: 175 year: 2018 end-page: 203 ident: b16 article-title: Design of extended dissipativity state estimation for generalized neural networks with mixed time-varying delay signals publication-title: Information Sciences – volume: 20 start-page: 3296 year: 2011 end-page: 3301 ident: b43 article-title: Cellular neural networks, the Navier–Stokes equation, and microarray image reconstruction publication-title: IEEE Transactions on Image Processing – volume: 27 start-page: 190 year: 2016 end-page: 201 ident: b2 article-title: Exponential synchronization of coupled stochastic memristor-based neural networks with time-varying probabilistic delay coupling and impulsive delay publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 39 start-page: 5625 year: 2012 end-page: 5633 ident: b19 article-title: Simplified stability criteria for fuzzy Markovian jumping Hopfield neural networks of neutral type with interval time-varying delays publication-title: Expert Systems with Applications – volume: 9 start-page: 381 year: 2015 end-page: 391 ident: b34 article-title: Synchronisation control of dynamical networks subject to variable sampling and actuators saturation publication-title: IET Control Theory Applications – volume: 11 start-page: 1112 year: 2017 end-page: 1123 ident: b25 article-title: Fault-tolerant SMC for Takagi–Sugeno fuzzy systems with time-varying delay and actuator saturation publication-title: IET Control Theory & Applications – volume: 290 start-page: 439 year: 2016 end-page: 459 ident: b8 article-title: Master-slave synchronization for nonlinear systems via reliable control with gaussian stochastic process publication-title: Applied Mathematics and Computation – volume: 62 start-page: 2330 year: 2015 end-page: 2340 ident: b10 article-title: Quantized control design for cognitive radio networks modeled as nonlinear semi-Markovian jump systems publication-title: IEEE Transactions on Industrial Electronics – volume: 55 start-page: 204 year: 2015 end-page: 208 ident: b20 article-title: Stability of time-delay systems via Wirtinger-based double integral inequality publication-title: Automatica – volume: 43 start-page: 1796 year: 2013 end-page: 1806 ident: b32 article-title: Stochastic synchronization of Markovian jump neural networks with time-varying delay using sampled data publication-title: IEEE Transactions on Cybernetics – volume: 90 start-page: 2057 year: 2017 end-page: 2068 ident: b14 article-title: Finite-/fixed-time robust stabilization of switched discontinuous systems with disturbances publication-title: Nonlinear Dynamics – volume: 43 start-page: 102 year: 2013 end-page: 114 ident: b15 article-title: Synchronization of coupled neutral-type neural networks with jumping-mode-dependent discrete and unbounded distributed delays publication-title: IEEE Transactions on Cybernetics – volume: 249 start-page: 64 year: 2017 end-page: 71 ident: b37 article-title: Finite-time synchronization of coupled time-delayed neural networks with discontinuous activations publication-title: Neurocomputing – volume: 219 start-page: 154 year: 2017 end-page: 162 ident: b39 article-title: Synchronization of coupled neural networks with time-varying delay publication-title: Neurocomputing – volume: 320 start-page: 235 year: 2015 end-page: 243 ident: b12 article-title: Exponential stabilization of singularly perturbed switched systems subject to actuator saturation publication-title: Information Sciences – volume: 141 start-page: 153 year: 2014 end-page: 159 ident: b40 article-title: Robust synchronization of coupled neural networks with mixed delays and uncertain parameters by intermittent pinning control publication-title: Neurocomputing – volume: 17 start-page: 3866 year: 2012 end-page: 3875 ident: b28 article-title: Adaptive synchronization in an array of linearly coupled neural networks with reaction–diffusion terms and time delays publication-title: Communications in Nonlinear Science and Numerical Simulation – volume: 45 start-page: 2680 year: 2015 end-page: 2692 ident: b1 article-title: Receding horizon stabilization and disturbance attenuation for neural networks with time-varying delay publication-title: IEEE Transactions on Cybernetics – volume: 54 start-page: 284 year: 2015 end-page: 291 ident: b30 article-title: Finite-time stabilization of high-order stochastic nonlinear systems in strict-feedback form publication-title: Automatica – volume: 10 start-page: 469 year: 2016 end-page: 476 ident: b38 article-title: Anti-windup controller design for singularly perturbed systems subject to actuator saturation publication-title: IET Control Theory Applications – year: 2015 ident: b22 article-title: Communication and control for networked complex systems – volume: 85 start-page: 128 year: 2017 end-page: 139 ident: b31 article-title: Finite-time synchronization of uncertain coupled switched neural networks under asynchronous switching publication-title: Neural Networks – volume: 47 start-page: 208 year: 2017 end-page: 226 ident: b3 article-title: Finite-time synchronization of master-slave neural networks with time-delays and discontinuous activations publication-title: Applied Mathematical Modelling – volume: 13 start-page: 868 year: 2016 end-page: 875 ident: b18 article-title: Disturbance observer-based adaptive tracking control with actuator saturation and its application publication-title: IEEE Transactions on Automation Science and Engineering – volume: 28 start-page: 2826 year: 2017 end-page: 2830 ident: b13 article-title: Discontinuous observers design for finite-time consensus of multiagent systems with external disturbances publication-title: IEEE Transactions on Neural Networks Learning Systems – volume: 47 start-page: 1875 year: 2017 end-page: 1883 ident: b27 article-title: Adaptive neural control of stochastic nonlinear time-delay systems with multiple constraints publication-title: IEEE Transactions on Systems, Man and Cybernetics, Systems – volume: 44 start-page: 2635 year: 2014 end-page: 2645 ident: b33 article-title: Local synchronization of chaotic neural networks with sampled-data and saturating actuators publication-title: IEEE Transactions on Cybernetics – volume: 25 start-page: 1804 year: 2014 end-page: 1815 ident: b26 article-title: Pinning distributed synchronization of stochastic dynamical networks: A mixed optimization approach publication-title: IEEE Transactions on Neural Networks and Learning Systems – year: 2015 ident: b29 article-title: Analysis and design of singular Markovian jump systems – volume: 219 start-page: 8009 year: 2013 end-page: 8017 ident: b5 article-title: Non-fragile synchronization of neural networks with time-varying delay and randomly occurring controller gain fluctuation publication-title: Applied Mathematics and Computation – volume: 28 start-page: 1618 year: 2017 end-page: 1632 ident: b4 article-title: Exponential synchronization for markovian stochastic coupled neural networks of neutral-type via adaptive feedback control publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 227 start-page: 82 year: 2017 end-page: 91 ident: b35 article-title: Synchronization for coupled reaction-diffusion neural networks with and without multiple time-varying delays via pinning-control publication-title: Neurocomputing – year: 1995 ident: b7 article-title: Neural network engineering in dynamic control systems – year: 2017 ident: b9 article-title: Control and filtering for semi-Markovian jump systems – volume: 139 start-page: 145 year: 2014 end-page: 149 ident: b6 article-title: Finite-time lag synchronization of delayed neural networks publication-title: Neurocomputing – volume: 185 start-page: 191 year: 2016 end-page: 201 ident: b24 article-title: Delay-range-dependent passivity analysis for uncertain stochastic neural networks with discrete and distributed time-varying delays publication-title: Neurocomputing – volume: 84 start-page: 2149 year: 2016 end-page: 2160 ident: b17 article-title: Finite-time boundedness and dissipativity analysis of networked cascade control systems publication-title: Nonlinear Dynamics – volume: 24 start-page: 1282 year: 2016 end-page: 1293 ident: b11 article-title: Output-feedback based sliding mode control for fuzzy systems with actuator saturation publication-title: IEEE Transactions on Fuzzy Systems – volume: 23 start-page: 467 year: 2012 end-page: 479 ident: b42 article-title: Stability analysis of Markovian jump stochastic BAM neural networks with impulse control and mixed time delays publication-title: IEEE Transactions on Neural Networks and Learning Systems – year: 2017 ident: 10.1016/j.neunet.2018.05.004_b9 – volume: 47 start-page: 208 year: 2017 ident: 10.1016/j.neunet.2018.05.004_b3 article-title: Finite-time synchronization of master-slave neural networks with time-delays and discontinuous activations publication-title: Applied Mathematical Modelling doi: 10.1016/j.apm.2017.03.024 – volume: 219 start-page: 154 year: 2017 ident: 10.1016/j.neunet.2018.05.004_b39 article-title: Synchronization of coupled neural networks with time-varying delay publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.09.004 – volume: 62 start-page: 2330 issue: 4 year: 2015 ident: 10.1016/j.neunet.2018.05.004_b10 article-title: Quantized control design for cognitive radio networks modeled as nonlinear semi-Markovian jump systems publication-title: IEEE Transactions on Industrial Electronics doi: 10.1109/TIE.2014.2351379 – volume: 219 start-page: 8009 issue: 15 year: 2013 ident: 10.1016/j.neunet.2018.05.004_b5 article-title: Non-fragile synchronization of neural networks with time-varying delay and randomly occurring controller gain fluctuation publication-title: Applied Mathematics and Computation doi: 10.1016/j.amc.2013.02.030 – volume: 55 start-page: 204 year: 2015 ident: 10.1016/j.neunet.2018.05.004_b20 article-title: Stability of time-delay systems via Wirtinger-based double integral inequality publication-title: Automatica doi: 10.1016/j.automatica.2015.03.010 – volume: 141 start-page: 153 year: 2014 ident: 10.1016/j.neunet.2018.05.004_b40 article-title: Robust synchronization of coupled neural networks with mixed delays and uncertain parameters by intermittent pinning control publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.03.042 – volume: 24 start-page: 1282 issue: 6 year: 2016 ident: 10.1016/j.neunet.2018.05.004_b11 article-title: Output-feedback based sliding mode control for fuzzy systems with actuator saturation publication-title: IEEE Transactions on Fuzzy Systems doi: 10.1109/TFUZZ.2015.2513085 – volume: 43 start-page: 102 issue: 1 year: 2013 ident: 10.1016/j.neunet.2018.05.004_b15 article-title: Synchronization of coupled neutral-type neural networks with jumping-mode-dependent discrete and unbounded distributed delays publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TSMCB.2012.2199751 – volume: 25 start-page: 1804 issue: 10 year: 2014 ident: 10.1016/j.neunet.2018.05.004_b26 article-title: Pinning distributed synchronization of stochastic dynamical networks: A mixed optimization approach publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2013.2295966 – volume: 20 start-page: 3296 issue: 11 year: 2011 ident: 10.1016/j.neunet.2018.05.004_b43 article-title: Cellular neural networks, the Navier–Stokes equation, and microarray image reconstruction publication-title: IEEE Transactions on Image Processing doi: 10.1109/TIP.2011.2159231 – volume: 139 start-page: 145 year: 2014 ident: 10.1016/j.neunet.2018.05.004_b6 article-title: Finite-time lag synchronization of delayed neural networks publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.02.050 – volume: 39 start-page: 5625 issue: 5 year: 2012 ident: 10.1016/j.neunet.2018.05.004_b19 article-title: Simplified stability criteria for fuzzy Markovian jumping Hopfield neural networks of neutral type with interval time-varying delays publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2011.11.055 – volume: 23 start-page: 467 issue: 3 year: 2012 ident: 10.1016/j.neunet.2018.05.004_b42 article-title: Stability analysis of Markovian jump stochastic BAM neural networks with impulse control and mixed time delays publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2011.2182659 – volume: 28 start-page: 1618 issue: 7 year: 2017 ident: 10.1016/j.neunet.2018.05.004_b4 article-title: Exponential synchronization for markovian stochastic coupled neural networks of neutral-type via adaptive feedback control publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2016.2546962 – volume: 85 start-page: 128 year: 2017 ident: 10.1016/j.neunet.2018.05.004_b31 article-title: Finite-time synchronization of uncertain coupled switched neural networks under asynchronous switching publication-title: Neural Networks doi: 10.1016/j.neunet.2016.10.007 – volume: 290 start-page: 439 year: 2016 ident: 10.1016/j.neunet.2018.05.004_b8 article-title: Master-slave synchronization for nonlinear systems via reliable control with gaussian stochastic process publication-title: Applied Mathematics and Computation doi: 10.1016/j.amc.2016.06.018 – volume: 249 start-page: 64 year: 2017 ident: 10.1016/j.neunet.2018.05.004_b37 article-title: Finite-time synchronization of coupled time-delayed neural networks with discontinuous activations publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.03.017 – year: 1995 ident: 10.1016/j.neunet.2018.05.004_b7 – volume: 13 start-page: 868 issue: 2 year: 2016 ident: 10.1016/j.neunet.2018.05.004_b18 article-title: Disturbance observer-based adaptive tracking control with actuator saturation and its application publication-title: IEEE Transactions on Automation Science and Engineering doi: 10.1109/TASE.2015.2414652 – volume: 275 start-page: 213 year: 2016 ident: 10.1016/j.neunet.2018.05.004_b23 article-title: Reliable anti-synchronization conditions for BAM memristive neural networks with different memductance functions publication-title: Applied Mathematics and Computation doi: 10.1016/j.amc.2015.11.060 – volume: 17 start-page: 3866 issue: 10 year: 2012 ident: 10.1016/j.neunet.2018.05.004_b28 article-title: Adaptive synchronization in an array of linearly coupled neural networks with reaction–diffusion terms and time delays publication-title: Communications in Nonlinear Science and Numerical Simulation doi: 10.1016/j.cnsns.2012.02.020 – volume: 185 start-page: 191 year: 2016 ident: 10.1016/j.neunet.2018.05.004_b24 article-title: Delay-range-dependent passivity analysis for uncertain stochastic neural networks with discrete and distributed time-varying delays publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.12.056 – year: 2015 ident: 10.1016/j.neunet.2018.05.004_b29 – volume: 227 start-page: 82 year: 2017 ident: 10.1016/j.neunet.2018.05.004_b35 article-title: Synchronization for coupled reaction-diffusion neural networks with and without multiple time-varying delays via pinning-control publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.10.063 – volume: 11 start-page: 1112 issue: 8 year: 2017 ident: 10.1016/j.neunet.2018.05.004_b25 article-title: Fault-tolerant SMC for Takagi–Sugeno fuzzy systems with time-varying delay and actuator saturation publication-title: IET Control Theory & Applications doi: 10.1049/iet-cta.2016.0845 – volume: 41 start-page: 341 issue: 2 year: 2011 ident: 10.1016/j.neunet.2018.05.004_b41 article-title: Exponential stability of stochastic neural networks with both Markovian jump parameters and mixed time delays publication-title: IEEE Transactions on Systems, Man and Cybernetics: Part B doi: 10.1109/TSMCB.2010.2053354 – volume: 45 start-page: 2680 issue: 12 year: 2015 ident: 10.1016/j.neunet.2018.05.004_b1 article-title: Receding horizon stabilization and disturbance attenuation for neural networks with time-varying delay publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2014.2381604 – volume: 62 start-page: 2916 issue: 6 year: 2017 ident: 10.1016/j.neunet.2018.05.004_b21 article-title: Weighted consensus protocols design based on network centrality for multi-agent systems with sampled-data publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.2016.2604682 – volume: 54 start-page: 284 year: 2015 ident: 10.1016/j.neunet.2018.05.004_b30 article-title: Finite-time stabilization of high-order stochastic nonlinear systems in strict-feedback form publication-title: Automatica doi: 10.1016/j.automatica.2015.02.016 – volume: 28 start-page: 2826 issue: 11 year: 2017 ident: 10.1016/j.neunet.2018.05.004_b13 article-title: Discontinuous observers design for finite-time consensus of multiagent systems with external disturbances publication-title: IEEE Transactions on Neural Networks Learning Systems doi: 10.1109/TNNLS.2016.2599199 – volume: 84 start-page: 2149 issue: 4 year: 2016 ident: 10.1016/j.neunet.2018.05.004_b17 article-title: Finite-time boundedness and dissipativity analysis of networked cascade control systems publication-title: Nonlinear Dynamics doi: 10.1007/s11071-016-2635-2 – volume: 47 start-page: 1875 issue: 8 year: 2017 ident: 10.1016/j.neunet.2018.05.004_b27 article-title: Adaptive neural control of stochastic nonlinear time-delay systems with multiple constraints publication-title: IEEE Transactions on Systems, Man and Cybernetics, Systems doi: 10.1109/TSMC.2016.2562511 – volume: 9 start-page: 381 issue: 3 year: 2015 ident: 10.1016/j.neunet.2018.05.004_b34 article-title: Synchronisation control of dynamical networks subject to variable sampling and actuators saturation publication-title: IET Control Theory Applications doi: 10.1049/iet-cta.2014.0383 – year: 2015 ident: 10.1016/j.neunet.2018.05.004_b22 – volume: 10 start-page: 469 issue: 4 year: 2016 ident: 10.1016/j.neunet.2018.05.004_b38 article-title: Anti-windup controller design for singularly perturbed systems subject to actuator saturation publication-title: IET Control Theory Applications doi: 10.1049/iet-cta.2015.0189 – volume: 27 start-page: 190 issue: 1 year: 2016 ident: 10.1016/j.neunet.2018.05.004_b2 article-title: Exponential synchronization of coupled stochastic memristor-based neural networks with time-varying probabilistic delay coupling and impulsive delay publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2015.2475737 – volume: 424 start-page: 175 year: 2018 ident: 10.1016/j.neunet.2018.05.004_b16 article-title: Design of extended dissipativity state estimation for generalized neural networks with mixed time-varying delay signals publication-title: Information Sciences doi: 10.1016/j.ins.2017.10.007 – volume: 320 start-page: 235 year: 2015 ident: 10.1016/j.neunet.2018.05.004_b12 article-title: Exponential stabilization of singularly perturbed switched systems subject to actuator saturation publication-title: Information Sciences doi: 10.1016/j.ins.2015.05.031 – volume: 60 start-page: 363 issue: 2 year: 2013 ident: 10.1016/j.neunet.2018.05.004_b36 article-title: Synchronization of randomly coupled neural networks with Markovian jumping and time-delay publication-title: IEEE Transactions on Circuits & Systems I: Regular Papers doi: 10.1109/TCSI.2012.2215804 – volume: 90 start-page: 2057 issue: 3 year: 2017 ident: 10.1016/j.neunet.2018.05.004_b14 article-title: Finite-/fixed-time robust stabilization of switched discontinuous systems with disturbances publication-title: Nonlinear Dynamics doi: 10.1007/s11071-017-3782-9 – volume: 44 start-page: 2635 issue: 12 year: 2014 ident: 10.1016/j.neunet.2018.05.004_b33 article-title: Local synchronization of chaotic neural networks with sampled-data and saturating actuators publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2014.2312004 – volume: 43 start-page: 1796 issue: 6 year: 2013 ident: 10.1016/j.neunet.2018.05.004_b32 article-title: Stochastic synchronization of Markovian jump neural networks with time-varying delay using sampled data publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TSMCB.2012.2230441 |
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| SubjectTerms | Coupled stochastic neural networks Finite-time synchronization Markovian jumping parameters Saturation effect Stochastic coupling strength |
| Title | Finite-time synchronization of stochastic coupled neural networks subject to Markovian switching and input saturation |
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