Finite/fixed-time synchronization of inertial memristive neural networks by interval matrix method for secure communication
This paper investigates the finite/fixed-time synchronization problem of delayed inertial memristive neural networks (DIMNNs) using interval matrix-based methods within a unified control framework. By employing set-valued mapping and differential inclusion theory, two distinct methods are applied to...
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| Published in | Neural Networks Vol. 167; pp. 168 - 182 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English Japanese |
| Published |
Elsevier Ltd
01.10.2023
Elsevier BV |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0893-6080 1879-2782 1879-2782 |
| DOI | 10.1016/j.neunet.2023.08.015 |
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| Abstract | This paper investigates the finite/fixed-time synchronization problem of delayed inertial memristive neural networks (DIMNNs) using interval matrix-based methods within a unified control framework. By employing set-valued mapping and differential inclusion theory, two distinct methods are applied to handle the switching behavior of memristor parameters: the maximum absolute value method and the interval matrix method. Based on these different approaches, two control strategies are proposed to select appropriate control parameters, enabling the system to achieve finite and fixed-time synchronization, respectively. Additionally, the resulting theoretical criteria differ based on the chosen control strategy, with one expressed in algebraic form and the other in the form of linear matrix inequalities (LMIs). Numerical simulations demonstrate that the interval matrix method outperforms the maximum absolute value method in terms of handling memristor parameter switching, achieving faster finite/fixed-time synchronization. Furthermore, the theoretical results are extended to the field of image encryption, where the response system is utilized for decryption and expanding the keyspace.
•Inertial memristive neural networks (IMNNs) employ two methods to manage switching behaviors: maximum absolute value and interval matrix.•Controllers based on these methods yield distinct theorems: one in algebraic form and the other in the form of linear matrix inequalities (LMIs).•Simulation shows Theorem 2 (interval matrix) yields faster synchronization convergence than Theorem 1 (maximum absolute value), and the findings are also applicable to image encryption. |
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| AbstractList | This paper investigates the finite/fixed-time synchronization problem of delayed inertial memristive neural networks (DIMNNs) using interval matrix-based methods within a unified control framework. By employing set-valued mapping and differential inclusion theory, two distinct methods are applied to handle the switching behavior of memristor parameters: the maximum absolute value method and the interval matrix method. Based on these different approaches, two control strategies are proposed to select appropriate control parameters, enabling the system to achieve finite and fixed-time synchronization, respectively. Additionally, the resulting theoretical criteria differ based on the chosen control strategy, with one expressed in algebraic form and the other in the form of linear matrix inequalities (LMIs). Numerical simulations demonstrate that the interval matrix method outperforms the maximum absolute value method in terms of handling memristor parameter switching, achieving faster finite/fixed-time synchronization. Furthermore, the theoretical results are extended to the field of image encryption, where the response system is utilized for decryption and expanding the keyspace.
•Inertial memristive neural networks (IMNNs) employ two methods to manage switching behaviors: maximum absolute value and interval matrix.•Controllers based on these methods yield distinct theorems: one in algebraic form and the other in the form of linear matrix inequalities (LMIs).•Simulation shows Theorem 2 (interval matrix) yields faster synchronization convergence than Theorem 1 (maximum absolute value), and the findings are also applicable to image encryption. This paper investigates the finite/fixed-time synchronization problem of delayed inertial memristive neural networks (DIMNNs) using interval matrix-based methods within a unified control framework. By employing set-valued mapping and differential inclusion theory, two distinct methods are applied to handle the switching behavior of memristor parameters: the maximum absolute value method and the interval matrix method. Based on these different approaches, two control strategies are proposed to select appropriate control parameters, enabling the system to achieve finite and fixed-time synchronization, respectively. Additionally, the resulting theoretical criteria differ based on the chosen control strategy, with one expressed in algebraic form and the other in the form of linear matrix inequalities (LMIs). Numerical simulations demonstrate that the interval matrix method outperforms the maximum absolute value method in terms of handling memristor parameter switching, achieving faster finite/fixed-time synchronization. Furthermore, the theoretical results are extended to the field of image encryption, where the response system is utilized for decryption and expanding the keyspace.This paper investigates the finite/fixed-time synchronization problem of delayed inertial memristive neural networks (DIMNNs) using interval matrix-based methods within a unified control framework. By employing set-valued mapping and differential inclusion theory, two distinct methods are applied to handle the switching behavior of memristor parameters: the maximum absolute value method and the interval matrix method. Based on these different approaches, two control strategies are proposed to select appropriate control parameters, enabling the system to achieve finite and fixed-time synchronization, respectively. Additionally, the resulting theoretical criteria differ based on the chosen control strategy, with one expressed in algebraic form and the other in the form of linear matrix inequalities (LMIs). Numerical simulations demonstrate that the interval matrix method outperforms the maximum absolute value method in terms of handling memristor parameter switching, achieving faster finite/fixed-time synchronization. Furthermore, the theoretical results are extended to the field of image encryption, where the response system is utilized for decryption and expanding the keyspace. |
| Author | Chen, Guici Gunasekaran, Nallappan Zeng, Zhigang Wei, Fei |
| Author_xml | – sequence: 1 givenname: Fei surname: Wei fullname: Wei, Fei email: weifei@mail.xhu.edu.cn organization: School of Science, Xihua University, Chengdu, 610039, China – sequence: 2 givenname: Guici orcidid: 0000-0002-3069-0829 surname: Chen fullname: Chen, Guici email: chenguici@wust.edu.cn organization: Hubei Province Key Laboratory of System Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan, 430065, China – sequence: 3 givenname: Zhigang orcidid: 0000-0003-4587-3588 surname: Zeng fullname: Zeng, Zhigang email: zgzeng@hust.edu.cn organization: School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China – sequence: 4 givenname: Nallappan orcidid: 0000-0003-0375-7917 surname: Gunasekaran fullname: Gunasekaran, Nallappan email: gunasmaths@gmail.com organization: The Computational Intelligence Laboratory, Toyota Technological Institute, Nagoya 468-8511, Japan |
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| Cites_doi | 10.1063/5.0079834 10.1109/TCT.1971.1083337 10.1016/j.jfranklin.2018.04.026 10.1038/nature06932 10.1007/s11071-018-4606-2 10.1109/TNNLS.2016.2561298 10.1016/j.neunet.2019.01.014 10.1109/TNNLS.2018.2852497 10.1007/s11571-017-9455-z 10.1007/BF00317936 10.1016/j.automatica.2017.06.008 10.1016/j.neucom.2019.03.040 10.1085/jgp.55.4.497 10.1155/2022/2006947 10.1016/j.knosys.2021.107395 10.1016/j.neunet.2017.02.001 10.1016/j.neunet.2017.10.003 10.1038/s41598-017-05480-0 10.1007/BF00197284 10.1103/PhysRevE.80.026206 10.1109/TCYB.2019.2953236 10.1007/s00500-022-07207-4 10.1109/TNNLS.2018.2874035 10.1016/j.neunet.2015.10.009 10.1038/s41563-019-0291-x 10.1038/s41586-021-04223-6 10.1109/TNNLS.2020.3006516 10.1016/j.neunet.2016.04.011 10.1109/TCYB.2019.2947859 10.1073/pnas.81.10.3088 10.1016/j.neucom.2017.05.075 10.1109/TCYB.2017.2676978 10.1109/TSMC.2018.2850157 10.1016/0167-2789(86)90152-1 10.1016/j.neucom.2019.07.036 10.1109/TAC.2011.2179869 10.1109/TVLSI.2019.2942267 10.1073/pnas.79.8.2554 10.1109/TCYB.2019.2957398 |
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| Keywords | Image encryption Finite/fixed-time synchronization Unified control framework Settling time functions Delayed inertial memristive neural networks (DIMNNs) |
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| References | Fan, Huang, Li, Xia, Chen (b8) 2019; 49 Yang, Cao, Liang (b42) 2017; 28 Sheng, Huang, Zeng, Li (b27) 2021; 51 Sakthivel, Pallavi, Ma, Vijayakumar (b25) 2022; 26 Shen, Wang, Cao, Lu, Song, Huang (b26) 2018; 30 Gong, Guo, Wen, Huang (b10) 2021; 51 Ji, Hu, Yu, Jiang (b15) 2018; 355 Hopfield (b11) 1982; 79 Wan, Cao, Wen, Yu (b31) 2016; 73 Ashmore, Attwell (b2) 1985; 226 Polyakov (b23) 2011; 57 Hu, Yu, Chen, Jiang, Huang (b13) 2017; 89 Lu, Liu, Chen (b18) 2016; 81 Parsegov, Polyakov, Shcherbakov (b21) 2012 Wei, Chen, Wang (b36) 2021; 230 Chua (b7) 1971; 18 Sakthivel, Ma, Mounika Devi, Manopriya, Vijayakumar, Huh (b24) 2022; 2022 Xiao, Zeng (b39) 2017; 47 Koch (b16) 1984; 50 Mauro, Conti, Dodge, Schor (b19) 1970; 55 Strukov, Snider, Stewart, Williams (b30) 2008; 453 Wang, Ge, Hu, Zhang (b32) 2019; 95 Xiao, Zeng, Wen, Wu, Wang (b40) 2019; 51 Wright, Onodera, Stein, Wang, Schachter, Hu (b37) 2022; 601 Huang, Jiang, Jian (b14) 2017; 266 Filippov (b9) 1964 Chen, Li, Peng, Yang, Mi, Wang (b6) 2019; 349 Hopfield (b12) 1984; 81 Sheng, Lewis, Zeng (b28) 2019; 30 Song, Wang, Holloway, Krstic (b29) 2017; 83 Xiao, Zeng, Wen, Wu, Wang (b41) 2021; 32 Babcock, Westervelt (b3) 1986; 23 Pedretti, Milo, Ambrogio, Carboni, Bianchi, Calderoni (b22) 2017; 7 Wang, Perc, Duan, Chen (b33) 2009; 80 Angelaki, Correia (b1) 1991; 65 Parastesh, Mehrabbeik, Rajagopal, Jafari, Perc (b20) 2022; 32 Li, Gao, Cao, Zhang (b17) 2019; 363 Wei, Cao (b34) 2019; 113 Zhang, Zeng, Hu (b43) 2018; 97 Chandrasekar, Radhika, Zhu (b5) 2022 Bai, An, Liu, Yi (b4) 2020; 28 Wei, Cao, Alsaedi (b35) 2018; 12 Xia, Yang (b38) 2019; 18 Yang (10.1016/j.neunet.2023.08.015_b42) 2017; 28 Gong (10.1016/j.neunet.2023.08.015_b10) 2021; 51 Wei (10.1016/j.neunet.2023.08.015_b34) 2019; 113 Parsegov (10.1016/j.neunet.2023.08.015_b21) 2012 Sheng (10.1016/j.neunet.2023.08.015_b27) 2021; 51 Chua (10.1016/j.neunet.2023.08.015_b7) 1971; 18 Wei (10.1016/j.neunet.2023.08.015_b35) 2018; 12 Angelaki (10.1016/j.neunet.2023.08.015_b1) 1991; 65 Lu (10.1016/j.neunet.2023.08.015_b18) 2016; 81 Ashmore (10.1016/j.neunet.2023.08.015_b2) 1985; 226 Xiao (10.1016/j.neunet.2023.08.015_b39) 2017; 47 Babcock (10.1016/j.neunet.2023.08.015_b3) 1986; 23 Li (10.1016/j.neunet.2023.08.015_b17) 2019; 363 Filippov (10.1016/j.neunet.2023.08.015_b9) 1964 Koch (10.1016/j.neunet.2023.08.015_b16) 1984; 50 Hopfield (10.1016/j.neunet.2023.08.015_b12) 1984; 81 Bai (10.1016/j.neunet.2023.08.015_b4) 2020; 28 Strukov (10.1016/j.neunet.2023.08.015_b30) 2008; 453 Song (10.1016/j.neunet.2023.08.015_b29) 2017; 83 Sakthivel (10.1016/j.neunet.2023.08.015_b24) 2022; 2022 Shen (10.1016/j.neunet.2023.08.015_b26) 2018; 30 Wei (10.1016/j.neunet.2023.08.015_b36) 2021; 230 Mauro (10.1016/j.neunet.2023.08.015_b19) 1970; 55 Chen (10.1016/j.neunet.2023.08.015_b6) 2019; 349 Chandrasekar (10.1016/j.neunet.2023.08.015_b5) 2022 Fan (10.1016/j.neunet.2023.08.015_b8) 2019; 49 Ji (10.1016/j.neunet.2023.08.015_b15) 2018; 355 Sakthivel (10.1016/j.neunet.2023.08.015_b25) 2022; 26 Wan (10.1016/j.neunet.2023.08.015_b31) 2016; 73 Huang (10.1016/j.neunet.2023.08.015_b14) 2017; 266 Wang (10.1016/j.neunet.2023.08.015_b32) 2019; 95 Zhang (10.1016/j.neunet.2023.08.015_b43) 2018; 97 Pedretti (10.1016/j.neunet.2023.08.015_b22) 2017; 7 Wang (10.1016/j.neunet.2023.08.015_b33) 2009; 80 Polyakov (10.1016/j.neunet.2023.08.015_b23) 2011; 57 Hu (10.1016/j.neunet.2023.08.015_b13) 2017; 89 Xiao (10.1016/j.neunet.2023.08.015_b40) 2019; 51 Xiao (10.1016/j.neunet.2023.08.015_b41) 2021; 32 Parastesh (10.1016/j.neunet.2023.08.015_b20) 2022; 32 Xia (10.1016/j.neunet.2023.08.015_b38) 2019; 18 Hopfield (10.1016/j.neunet.2023.08.015_b11) 1982; 79 Wright (10.1016/j.neunet.2023.08.015_b37) 2022; 601 Sheng (10.1016/j.neunet.2023.08.015_b28) 2019; 30 |
| References_xml | – volume: 51 start-page: 3004 year: 2019 end-page: 3016 ident: b40 article-title: A unified framework design for finite-time and fixed-time synchronization of discontinuous neural networks publication-title: IEEE Transactions on Cybernetics – volume: 73 start-page: 86 year: 2016 end-page: 94 ident: b31 article-title: Robust fixed-time synchronization of delayed cohen–grossberg neural networks publication-title: Neural Networks – volume: 23 start-page: 464 year: 1986 end-page: 469 ident: b3 article-title: Stability and dynamics of simple electronic neural networks with added inertia publication-title: Physica D: Nonlinear Phenomena – volume: 226 start-page: 325 year: 1985 end-page: 344 ident: b2 article-title: Models for electrical tuning in hair cells publication-title: Proceedings of the Royal Society of London. Series B. Biological Sciences – volume: 57 start-page: 2106 year: 2011 end-page: 2110 ident: b23 article-title: Nonlinear feedback design for fixed-time stabilization of linear control systems publication-title: IEEE Transactions on Automatic Control – volume: 83 start-page: 243 year: 2017 end-page: 251 ident: b29 article-title: Time-varying feedback for regulation of normal-form nonlinear systems in prescribed finite time publication-title: Automatica – volume: 32 year: 2022 ident: b20 article-title: Synchronization in hindmarsh–rose neurons subject to higher-order interactions publication-title: Chaos. An Interdisciplinary Journal of Nonlinear Science – volume: 81 start-page: 3088 year: 1984 end-page: -3092 ident: b12 article-title: Neurons with graded response have collective computational properties like those of two-state neurons publication-title: Proceedings of the National Academy of Sciences of the United States of America – start-page: 7732 year: 2012 end-page: 7737 ident: b21 article-title: Nonlinear fixed-time control protocol for uniform allocation of agents on a segment publication-title: 2012 IEEE 51st IEEE conference on decision and control – volume: 266 start-page: 527 year: 2017 end-page: 539 ident: b14 article-title: Finite-time synchronization of inertial memristive neural networks with time-varying delays via sampled-date control publication-title: Neurocomputing – volume: 28 start-page: 62 year: 2020 end-page: 75 ident: b4 article-title: A training-efficient hybrid-structured deep neural network with reconfigurable memristive synapses publication-title: IEEE Transactions on Very Large Scale Integration (VLSI) Systems – volume: 18 start-page: 507 year: 1971 end-page: 519 ident: b7 article-title: Memristor-the missing circuit element publication-title: IEEE Transactions on Circuit Theory – volume: 80 year: 2009 ident: b33 article-title: Synchronization transitions on scale-free neuronal networks due to finite information transmission delays publication-title: Physical Review E – volume: 363 start-page: 236 year: 2019 end-page: 245 ident: b17 article-title: Dissipativity and exponential state estimation for quaternion-valued memristive neural networks publication-title: Neurocomputing – volume: 65 start-page: 1 year: 1991 end-page: 10 ident: b1 article-title: Models of membrane resonance in pigeon semicircular canal type ii hair cells publication-title: Biological Cybernetics – volume: 601 start-page: 549 year: 2022 end-page: 555 ident: b37 article-title: Deep physical neural networks trained with backpropagation publication-title: Nature – volume: 50 start-page: 15 year: 1984 end-page: -33 ident: b16 article-title: Cable theory in neurons with active, linearized membranes publication-title: Biological Cybernetics – volume: 79 start-page: 2554 year: 1982 end-page: -2558 ident: b11 article-title: Neural networks and physical systems with emergent collective computational abilities publication-title: Proceedings of the National Academy of Sciences of the United States of America – volume: 30 start-page: 739 year: 2019 end-page: 750 ident: b28 article-title: Exponential stabilization of fuzzy memristive neural networks with hybrid unbounded time-varying delays publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 51 start-page: 579 year: 2021 end-page: 588 ident: b27 article-title: Exponential stabilization of inertial memristive neural networks with multiple time delays publication-title: IEEE Transactions on Cybernetics – volume: 2022 year: 2022 ident: b24 article-title: Nonuniform sampled-data control for synchronization of semi-markovian jump stochastic complex dynamical networks with time-varying delays publication-title: Complexity – volume: 349 start-page: 290 year: 2019 end-page: 300 ident: b6 article-title: A new fixed-time stability theorem and its application to the synchronization control of memristive neural networks publication-title: Neurocomputing – volume: 28 start-page: 1878 year: 2017 end-page: 1888 ident: b42 article-title: Exponential synchronization of memristive neural networks with delays: Interval matrix method publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 7 year: 2017 ident: b22 article-title: Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity publication-title: Scientific Reports – volume: 55 start-page: 497 year: 1970 end-page: -523 ident: b19 article-title: Subthreshold behavior and phenomenological impedance of the squid giant axon publication-title: The Journal of General Physiology – volume: 47 start-page: 2984 year: 2017 end-page: 2994 ident: b39 article-title: Scale-limited lagrange stability and finite-time synchronization for memristive recurrent neural networks on time scales publication-title: IEEE Transactions on Cybernetics – volume: 32 start-page: 2535 year: 2021 end-page: 2546 ident: b41 article-title: Finite-/fixed-time synchronization of delayed coupled discontinuous neural networks with unified control schemes publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 453 start-page: 80 year: 2008 end-page: 83 ident: b30 article-title: The missing memristor found publication-title: Nature – volume: 89 start-page: 74 year: 2017 end-page: 83 ident: b13 article-title: Fixed-time stability of dynamical systems and fixed-time synchronization of coupled discontinuous neural networks publication-title: Neural Networks – volume: 230 year: 2021 ident: b36 article-title: Finite-time stabilization of memristor-based inertial neural networks with time-varying delays combined with interval matrix method publication-title: Knowledge-Based Systems – volume: 95 start-page: 943 year: 2019 end-page: 955 ident: b32 article-title: Global stability and stabilization for inertial memristive neural networks with unbounded distributed delays publication-title: Nonlinear Dynamics – volume: 355 start-page: 4665 year: 2018 end-page: 4685 ident: b15 article-title: Finite-time and fixed-time synchronization of discontinuous complex networks: A unified control framework design publication-title: Journal of the Franklin Institute – volume: 30 start-page: 1841 year: 2018 end-page: 1853 ident: b26 article-title: Nonfragile dissipative synchronization for markovian memristive neural networks: A gain-scheduled control scheme publication-title: IEEE Transactions on Neural Networks and Learning Systems – start-page: 1 year: 2022 end-page: 19 ident: b5 article-title: State estimation for genetic regulatory networks with two delay components by using second-order reciprocally convex approach publication-title: Neural Processing Letters – year: 1964 ident: b9 article-title: Differential equations with discontinuous right-hand side – volume: 81 start-page: 11 year: 2016 end-page: 15 ident: b18 article-title: A note on finite-time and fixed-time stability publication-title: Neural Networks – volume: 49 start-page: 2254 year: 2019 end-page: 2265 ident: b8 article-title: Aperiodically intermittent control for quasi-synchronization of delayed memristive neural networks: An interval matrix and matrix measure combined method publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems – volume: 51 start-page: 2944 year: 2021 end-page: 2955 ident: b10 article-title: Finite-time and fixed-time synchronization of coupled memristive neural networks with time delay publication-title: IEEE Transactions on Cybernetics – volume: 113 start-page: 1 year: 2019 end-page: 10 ident: b34 article-title: Fixed-time synchronization of quaternion-valued memristive neural networks with time delays publication-title: Neural Networks – volume: 97 start-page: 183 year: 2018 end-page: 191 ident: b43 article-title: New results on global exponential dissipativity analysis of memristive inertial neural networks with distributed time-varying delays publication-title: Neural Networks – volume: 26 start-page: 8371 year: 2022 end-page: 8386 ident: b25 article-title: Finite-time dissipative synchronization of discrete-time semi-markovian jump complex dynamical networks with actuator faults publication-title: Soft Computing – volume: 12 year: 2018 ident: b35 article-title: Finite-time and fixed-time synchronization analysis of inertial memristive neural networks with time-varying delays publication-title: Cognitive Neurodynamics – volume: 18 start-page: 309 year: 2019 end-page: 323 ident: b38 article-title: Memristive crossbar arrays for brain-inspired computing publication-title: Nature Materials – volume: 32 year: 2022 ident: 10.1016/j.neunet.2023.08.015_b20 article-title: Synchronization in hindmarsh–rose neurons subject to higher-order interactions publication-title: Chaos. An Interdisciplinary Journal of Nonlinear Science doi: 10.1063/5.0079834 – volume: 18 start-page: 507 year: 1971 ident: 10.1016/j.neunet.2023.08.015_b7 article-title: Memristor-the missing circuit element publication-title: IEEE Transactions on Circuit Theory doi: 10.1109/TCT.1971.1083337 – volume: 355 start-page: 4665 year: 2018 ident: 10.1016/j.neunet.2023.08.015_b15 article-title: Finite-time and fixed-time synchronization of discontinuous complex networks: A unified control framework design publication-title: Journal of the Franklin Institute doi: 10.1016/j.jfranklin.2018.04.026 – volume: 453 start-page: 80 year: 2008 ident: 10.1016/j.neunet.2023.08.015_b30 article-title: The missing memristor found publication-title: Nature doi: 10.1038/nature06932 – volume: 95 start-page: 943 year: 2019 ident: 10.1016/j.neunet.2023.08.015_b32 article-title: Global stability and stabilization for inertial memristive neural networks with unbounded distributed delays publication-title: Nonlinear Dynamics doi: 10.1007/s11071-018-4606-2 – volume: 28 start-page: 1878 year: 2017 ident: 10.1016/j.neunet.2023.08.015_b42 article-title: Exponential synchronization of memristive neural networks with delays: Interval matrix method publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2016.2561298 – volume: 113 start-page: 1 year: 2019 ident: 10.1016/j.neunet.2023.08.015_b34 article-title: Fixed-time synchronization of quaternion-valued memristive neural networks with time delays publication-title: Neural Networks doi: 10.1016/j.neunet.2019.01.014 – volume: 30 start-page: 739 year: 2019 ident: 10.1016/j.neunet.2023.08.015_b28 article-title: Exponential stabilization of fuzzy memristive neural networks with hybrid unbounded time-varying delays publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2018.2852497 – volume: 12 year: 2018 ident: 10.1016/j.neunet.2023.08.015_b35 article-title: Finite-time and fixed-time synchronization analysis of inertial memristive neural networks with time-varying delays publication-title: Cognitive Neurodynamics doi: 10.1007/s11571-017-9455-z – volume: 50 start-page: 15 year: 1984 ident: 10.1016/j.neunet.2023.08.015_b16 article-title: Cable theory in neurons with active, linearized membranes publication-title: Biological Cybernetics doi: 10.1007/BF00317936 – volume: 83 start-page: 243 year: 2017 ident: 10.1016/j.neunet.2023.08.015_b29 article-title: Time-varying feedback for regulation of normal-form nonlinear systems in prescribed finite time publication-title: Automatica doi: 10.1016/j.automatica.2017.06.008 – volume: 349 start-page: 290 year: 2019 ident: 10.1016/j.neunet.2023.08.015_b6 article-title: A new fixed-time stability theorem and its application to the synchronization control of memristive neural networks publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.03.040 – volume: 55 start-page: 497 year: 1970 ident: 10.1016/j.neunet.2023.08.015_b19 article-title: Subthreshold behavior and phenomenological impedance of the squid giant axon publication-title: The Journal of General Physiology doi: 10.1085/jgp.55.4.497 – volume: 2022 year: 2022 ident: 10.1016/j.neunet.2023.08.015_b24 article-title: Nonuniform sampled-data control for synchronization of semi-markovian jump stochastic complex dynamical networks with time-varying delays publication-title: Complexity doi: 10.1155/2022/2006947 – start-page: 1 year: 2022 ident: 10.1016/j.neunet.2023.08.015_b5 article-title: State estimation for genetic regulatory networks with two delay components by using second-order reciprocally convex approach publication-title: Neural Processing Letters – volume: 230 year: 2021 ident: 10.1016/j.neunet.2023.08.015_b36 article-title: Finite-time stabilization of memristor-based inertial neural networks with time-varying delays combined with interval matrix method publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2021.107395 – volume: 89 start-page: 74 year: 2017 ident: 10.1016/j.neunet.2023.08.015_b13 article-title: Fixed-time stability of dynamical systems and fixed-time synchronization of coupled discontinuous neural networks publication-title: Neural Networks doi: 10.1016/j.neunet.2017.02.001 – volume: 97 start-page: 183 year: 2018 ident: 10.1016/j.neunet.2023.08.015_b43 article-title: New results on global exponential dissipativity analysis of memristive inertial neural networks with distributed time-varying delays publication-title: Neural Networks doi: 10.1016/j.neunet.2017.10.003 – volume: 7 year: 2017 ident: 10.1016/j.neunet.2023.08.015_b22 article-title: Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity publication-title: Scientific Reports doi: 10.1038/s41598-017-05480-0 – volume: 65 start-page: 1 year: 1991 ident: 10.1016/j.neunet.2023.08.015_b1 article-title: Models of membrane resonance in pigeon semicircular canal type ii hair cells publication-title: Biological Cybernetics doi: 10.1007/BF00197284 – volume: 80 year: 2009 ident: 10.1016/j.neunet.2023.08.015_b33 article-title: Synchronization transitions on scale-free neuronal networks due to finite information transmission delays publication-title: Physical Review E doi: 10.1103/PhysRevE.80.026206 – volume: 51 start-page: 2944 year: 2021 ident: 10.1016/j.neunet.2023.08.015_b10 article-title: Finite-time and fixed-time synchronization of coupled memristive neural networks with time delay publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2019.2953236 – volume: 26 start-page: 8371 year: 2022 ident: 10.1016/j.neunet.2023.08.015_b25 article-title: Finite-time dissipative synchronization of discrete-time semi-markovian jump complex dynamical networks with actuator faults publication-title: Soft Computing doi: 10.1007/s00500-022-07207-4 – volume: 30 start-page: 1841 year: 2018 ident: 10.1016/j.neunet.2023.08.015_b26 article-title: Nonfragile dissipative synchronization for markovian memristive neural networks: A gain-scheduled control scheme publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2018.2874035 – volume: 73 start-page: 86 year: 2016 ident: 10.1016/j.neunet.2023.08.015_b31 article-title: Robust fixed-time synchronization of delayed cohen–grossberg neural networks publication-title: Neural Networks doi: 10.1016/j.neunet.2015.10.009 – volume: 18 start-page: 309 year: 2019 ident: 10.1016/j.neunet.2023.08.015_b38 article-title: Memristive crossbar arrays for brain-inspired computing publication-title: Nature Materials doi: 10.1038/s41563-019-0291-x – volume: 601 start-page: 549 year: 2022 ident: 10.1016/j.neunet.2023.08.015_b37 article-title: Deep physical neural networks trained with backpropagation publication-title: Nature doi: 10.1038/s41586-021-04223-6 – volume: 32 start-page: 2535 year: 2021 ident: 10.1016/j.neunet.2023.08.015_b41 article-title: Finite-/fixed-time synchronization of delayed coupled discontinuous neural networks with unified control schemes publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2020.3006516 – year: 1964 ident: 10.1016/j.neunet.2023.08.015_b9 – volume: 226 start-page: 325 year: 1985 ident: 10.1016/j.neunet.2023.08.015_b2 article-title: Models for electrical tuning in hair cells publication-title: Proceedings of the Royal Society of London. Series B. Biological Sciences – volume: 81 start-page: 11 year: 2016 ident: 10.1016/j.neunet.2023.08.015_b18 article-title: A note on finite-time and fixed-time stability publication-title: Neural Networks doi: 10.1016/j.neunet.2016.04.011 – volume: 51 start-page: 579 year: 2021 ident: 10.1016/j.neunet.2023.08.015_b27 article-title: Exponential stabilization of inertial memristive neural networks with multiple time delays publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2019.2947859 – volume: 81 start-page: 3088 year: 1984 ident: 10.1016/j.neunet.2023.08.015_b12 article-title: Neurons with graded response have collective computational properties like those of two-state neurons publication-title: Proceedings of the National Academy of Sciences of the United States of America doi: 10.1073/pnas.81.10.3088 – volume: 266 start-page: 527 year: 2017 ident: 10.1016/j.neunet.2023.08.015_b14 article-title: Finite-time synchronization of inertial memristive neural networks with time-varying delays via sampled-date control publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.05.075 – volume: 47 start-page: 2984 year: 2017 ident: 10.1016/j.neunet.2023.08.015_b39 article-title: Scale-limited lagrange stability and finite-time synchronization for memristive recurrent neural networks on time scales publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2017.2676978 – volume: 49 start-page: 2254 year: 2019 ident: 10.1016/j.neunet.2023.08.015_b8 article-title: Aperiodically intermittent control for quasi-synchronization of delayed memristive neural networks: An interval matrix and matrix measure combined method publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems doi: 10.1109/TSMC.2018.2850157 – volume: 23 start-page: 464 year: 1986 ident: 10.1016/j.neunet.2023.08.015_b3 article-title: Stability and dynamics of simple electronic neural networks with added inertia publication-title: Physica D: Nonlinear Phenomena doi: 10.1016/0167-2789(86)90152-1 – volume: 363 start-page: 236 year: 2019 ident: 10.1016/j.neunet.2023.08.015_b17 article-title: Dissipativity and exponential state estimation for quaternion-valued memristive neural networks publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.07.036 – volume: 57 start-page: 2106 year: 2011 ident: 10.1016/j.neunet.2023.08.015_b23 article-title: Nonlinear feedback design for fixed-time stabilization of linear control systems publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.2011.2179869 – volume: 28 start-page: 62 year: 2020 ident: 10.1016/j.neunet.2023.08.015_b4 article-title: A training-efficient hybrid-structured deep neural network with reconfigurable memristive synapses publication-title: IEEE Transactions on Very Large Scale Integration (VLSI) Systems doi: 10.1109/TVLSI.2019.2942267 – volume: 79 start-page: 2554 year: 1982 ident: 10.1016/j.neunet.2023.08.015_b11 article-title: Neural networks and physical systems with emergent collective computational abilities publication-title: Proceedings of the National Academy of Sciences of the United States of America doi: 10.1073/pnas.79.8.2554 – volume: 51 start-page: 3004 year: 2019 ident: 10.1016/j.neunet.2023.08.015_b40 article-title: A unified framework design for finite-time and fixed-time synchronization of discontinuous neural networks publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2019.2957398 – start-page: 7732 year: 2012 ident: 10.1016/j.neunet.2023.08.015_b21 article-title: Nonlinear fixed-time control protocol for uniform allocation of agents on a segment |
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| SubjectTerms | Algorithms Communication Delayed inertial memristive neural networks (DIMNNs) Finite/fixed-time synchronization Image encryption Neural Networks, Computer Settling time functions Time Factors Unified control framework |
| Title | Finite/fixed-time synchronization of inertial memristive neural networks by interval matrix method for secure communication |
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