274
Views
1
CrossRef citations to date
0
Altmetric
Review Articles

Synchronization for Markovian master-slave neural networks: an event-triggered impulsive approach

, , , & ORCID Icon
Pages 2551-2565 | Received 26 Jun 2022, Accepted 04 Sep 2022, Published online: 06 Oct 2022

References

  • Ahmad, B., Alghanmi, M., Alsaedi, A., & Agarwal, R. P. (2020). On an impulsive hybrid system of conformable fractional differential equations with boundary conditions. International Journal of Systems Science, 51(5), 958–970. https://doi.org/10.1080/00207721.2020.1746437
  • Alsaadi, F. E., Luo, Y., Liu, Y., & Wang, Z. (2018). State estimation for delayed neural networks with stochastic communication protocol: The finite-time case. Neurocomputing, 281, 86–95. https://doi.org/10.1016/j.neucom.2017.11.067
  • Alsaadi, F. E., Wang, Z., Wang, D., Alsaadi, F. E., & Alsaade, F. E. (2020). Recursive fusion estimation for stochastic discrete time-varying complex networks under stochastic communication protocol: The state-saturated case. Information Fusion, 60, 11–19. https://doi.org/10.1016/j.inffus.2020.01.012
  • Boyd, S., El Ghaoui, L, Feron, E., & Balakrishnan, V. (1994). Linear matrix inequalities in system and control theory. SIAM. https://doi.org/10.1109/jproc.1998.735454
  • Chen, W., Lu, X., & Zheng, W. X. (2014). Impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks. IEEE Transactions on Neural Networks and Learning Systems, 26(4), 734–748. https://doi.org/10.1109/TNNLS.2014.2322499
  • Chen, Y., Chen, Z., Chen, Z., & Xue, A. (2020). Observer-based passive control of non-homogeneous Markov jump systems with random communication delays. International Journal of Systems Science, 51(6), 1133–1147. https://doi.org/10.1080/00207721.2020.1752844
  • Chen, Y., Wang, Z., Hu, J., & Han, Q. (2020). Synchronization control for discrete-time-delayed dynamical networks with switching topology under actuator saturations. IEEE Transactions on Neural Networks and Learning Systems, 32(5), 2040–2053. https://doi.org/10.1109/TNNLS.2020.2996094
  • Cheng, G., Ju, Y., & Mu, X. (2021). Stochastic finite-time stability and stabilisation of semi-Markovian jump linear systems with generally uncertain transition rates. International Journal of Systems Science, 52(1), 185–195. https://doi.org/10.1080/00207721.2020.1823518
  • Cheng, J., Park, J. H., Zhao, X., Karimi, H. R., & Cao, J. (2019). Quantized nonstationary filtering of networked Markov switching rsnss: A multiple hierarchical structure strategy. IEEE Transactions on Automatic Control, 65(11), 4816–4823. https://doi.org/10.1109/TAC.9
  • Ding, D., Wang, Z., & Han, Q. (2019). A scalable algorithm for event-triggered state estimation with unknown parameters and switching topologies over sensor networks. IEEE Transactions on Cybernetics, 50(9), 4087–4097. https://doi.org/10.1109/TCYB.6221036
  • Elhaki, O., & Shojaei, K. (2021). Robust prescribed performance-based control of autonomous tractor-trailers convoy with limited communication range. International Journal of Systems Science, 52(3), 555–582. https://doi.org/10.1080/00207721.2020.1834004
  • Fang, J., Zhang, Y., Xu, D., & Sun, J. (2020). Synchronization of time delay coupled neural networks based on impulsive control. Mathematical Problems in Engineering. https://doi.org/10.1155/2020/5986018
  • Ho, D. W., & Lu, G. (2003). Robust stabilization for a class of discrete-time non-linear systems via output feedback: The unified LMI approach. International Journal of Control, 76(2), 105–115. https://doi.org/10.1080/0020717031000067367
  • Hou, N., Dong, H., Wang, Z., Ren, W., & Alsaadi, F. E. (2016). Non-fragile state estimation for discrete Markovian jumping neural networks. Neurocomputing, 179, 238–245. https://doi.org/10.1016/j.neucom.2015.11.089
  • Huang, X., & Ma, Y. (2020). Sampled-data sliding mode exponential synchronisation of master–slave Markovian jump complex networks with nonlinear perturbation. International Journal of Systems Science, 51(10), 1714–1732. https://doi.org/10.1080/00207721.2020.1773957
  • Ji, X., Lu, J., Jiang, B., & Shi, K. (2021). Distributed synchronization of delayed neural networks: Delay-dependent hybrid impulsive control. IEEE Transactions on Network Science and Engineering, 9(2), 634–647. https://doi.org/10.1109/TNSE.2021.3128244
  • Jiang, B., Lu, J., Li, X., & Qiu, J. (2021). Event-triggered impulsive stabilization of systems with external disturbances. IEEE Transactions on Automatic Control, 67(4), 2116–2122. https://doi.org/10.1109/TAC.2021.3108123
  • Li, H., Fang, J. A., Li, X., Rutkowski, L., & Huang, T. (2022). Event-triggered synchronization of multiple discrete-time Markovian jump memristor-based neural networks with mixed mode-dependent delays. IEEE Transactions on Circuits and Systems I: Regular Papers, 69(5), 2095–2107. https://doi.org/10.1109/TCSI.2022.3149535
  • Li, Q., Shen, B., Wang, Z., Huang, T., & Luo, J. (2018). Synchronization control for a class of discrete time-delay complex dynamical networks: A dynamic event-triggered approach. IEEE Transactions on Cybernetics, 49(5), 1979–1986. https://doi.org/10.1109/TCYB.6221036
  • Li, X., Yang, X., & Cao, J. (2020). Event-triggered impulsive control for nonlinear delay systems. Automatica, 117, Article 108981. https://doi.org/10.1016/j.automatica.2020.108981
  • Li, Z., & Li, S. (2022). Model-based recurrent neural network for redundancy resolution of manipulator with remote centre of motion constraints. International Journal of Systems Science, Advance Online Publication. 1–14. https://doi.org/10.1080/00207721.2022.2070790
  • Lin, H., Wang, C., Chen, C., Sun, Y., Zhou, C., Xu, C., & Hong, Q. (2021). Neural bursting and synchronization emulated by neural networks and circuits. IEEE Transactions on Circuits and Systems I: Regular Papers, 68(8), 3397–3410. https://doi.org/10.1109/TCSI.2021.3081150
  • Lu, R., Xu, Y., Xue, A., & Zheng, J. (2012). Networked control with state reset and quantized measurements: Observer-based case. IEEE Transactions on Industrial Electronics, 60(11), 5206–5213. https://doi.org/10.1109/TIE.2012.2227910
  • Rao, H., Xu, Y., Peng, H., Lu, R., & Su, C. (2019). Quasi-synchronization of time delay Markovian jump neural networks with impulsive-driven transmission and fading channels. IEEE Transactions on Cybernetics, 50(9), 4121–4131. https://doi.org/10.1109/TCYB.6221036
  • Ren, L., Liu, Y., Huang, D., Huang, K., & Yang, C. (2022). MCTAN: A novel multichannel temporal attention-based network for industrial health indicator prediction. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2021.3136768
  • Ren, L., Wang, T., Laili, Y., & Zhang, L. (2021). A data-driven self-supervised LSTM-deepFM model for industrial soft sensor. IEEE Transactions on Industrial Informatics, 18(9), 5859–5869. https://doi.org/10.1109/TII.2021.3131471
  • Sanchez, E. N., & Perez, J. P. (1999). Input-to-state stability (ISS) analysis for dynamic neural networks. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 46(11), 1395–1398. https://doi.org/10.1109/81.802844
  • Shanmugasundaram, S., Udhayakumar, K., Gunasekaran, D., & Rakkiyappan, R. (2022). Event-triggered impulsive control design for synchronization of inertial neural networks with time delays. Neurocomputing, 483, 322–332. https://doi.org/10.1016/j.neucom.2022.02.023
  • Shao, H.-Y., Hu, A.-H., & Liu, D. (2015). Synchronization of Markovian jumping complex networks with event-triggered control. Chinese Physics B, 24(9), Article 098902. https://doi.org/10.1088/1674-1056/24/9/098902
  • Suykens, J. A., Curran, P. F., & Chua, L. (1999). Robust synthesis for master-slave synchronization of Lur'e systems. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 46(7), 841–850. https://doi.org/10.1109/81.774230
  • Syed Ali, M., Usha, M., Cao, J., & Lu, G. (2019). Synchronisation analysis for stochastic T–S fuzzy complex networks with coupling delay. International Journal of Systems Science, 50(3), 585–598. https://doi.org/10.1080/00207721.2018.1563731
  • Tang, W., Li, K., Wu, J., & Xie, Y. (2022). Consensus of nonlinear multi–agent systems with distributed event–triggered impulsive control. Journal of Vibration and Control, 28(7-8), 882–891. https://doi.org/10.1177/1077546320985978
  • Tao, J., Xiao, Z., Li, Z., Wu, J., Lu, R., Shi, P., & Wang, X. (2021). Dynamic event-triggered state estimation for Markov jump neural networks with partially unknown probabilities. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2021.3085001
  • Wang, D., Wang, Z., Wang, Z., & Wang, W. (2020). Design of hybrid event-triggered containment controllers for homogeneous and heterogeneous multiagent systems. IEEE Transactions on Cybernetics, 51(10), 4885–4896. https://doi.org/10.1109/TCYB.2020.3007500
  • Wang, J., Wang, Z., Chen, X., & Qiu, J. (2021). Synchronization criteria of delayed inertial neural networks with generally Markovian jumping. Neural Networks, 139, 64–76. https://doi.org/10.1016/j.neunet.2021.02.004
  • Xie, X., Wei, T., & Li, X. (2021). Hybrid event-triggered approach for quasi-consensus of uncertain multi-agent systems with impulsive protocols. IEEE Transactions on Circuits and Systems I: Regular Papers, 69(2), 872–883. https://doi.org/10.1109/TCSI.2021.3119065
  • Xu, Y., Huang, Z., Rao, H., Lu, R., & Huang, T. (2019). Quasi-synchronization for periodic neural networks with asynchronous target and constrained information. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(7), 4379–4388. https://doi.org/10.1109/TSMC.2019.2930971
  • Xu, Y., Yao, Z., Lu, R., & Ghosh, B. (2021). A novel fixed-time protocol for first-order consensus tracking with disturbance rejection. IEEE Transactions on Automatic Control. https://doi.org/10.1109/TAC.2021.3131549
  • Yang, H., Wang, Z., Shen, Y., & Alsaadi, F. E. (2021). Self-triggered filter design for a class of nonlinear stochastic systems with Markovian jumping parameters. Nonlinear Analysis: Hybrid Systems, 40. https://doi.org/10.1016/j.nahs.2021.101022
  • Zha, J., Li, C., Song, B., & Hu, W. (2016). Synchronisation control of composite chaotic systems. International Journal of Systems Science, 47(16), 3952–3959. https://doi.org/10.1080/00207721.2016.1157224
  • Zhang, H., Wang, Z., & Liu, D. (2014). A comprehensive review of stability analysis of continuous-time recurrent neural networks. IEEE Transactions on Neural Networks and Learning Systems, 25(7), 1229–1262. https://doi.org/10.1109/TNNLS.5962385
  • Zhang, J., & Peng, C. (2016). Synchronization of master–slave neural networks with a decentralized event triggered communication scheme. Neurocomputing, 173, 1824–1831. https://doi.org/10.1016/j.neucom.2015.09.058
  • Zhao, M., & Niu, Y. (2021). Parameter-dependent sliding mode control for Markovian jump systems within finite-time interval: Handling randomly occurring actuator faults. International Journal of Systems Science, 52(14), 2988–3000. https://doi.org/10.1080/00207721.2021.1916641
  • Zhao, Z., Wang, Z., Zou, L., Chen, Y., & Sheng, W. (2021). Event-triggered set-membership state estimation for complex networks: A zonotopes-based method. IEEE Transactions on Network Science and Engineering, 9(3), 1175–1186. https://doi.org/10.1109/TNSE.2021.3137320
  • Zou, L., Wang, Z., Geng, H., & Liu, X. (2021). Set-membership filtering subject to impulsive measurement outliers: A recursive algorithm. IEEE/CAA Journal of Automatica Sinica, 8(2), 377–388. https://doi.org/10.1109/JAS.6570654
  • Zou, L., Wang, Z., Hu, J., & Dong, H. (2022). Partial-node-based state estimation for delayed complex networks under intermittent measurement outliers: A multiple-order-holder approach. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2021.3138979
  • Zou, L., Wang, Z., Hu, J., Liu, Y., & Liu, X. (2021). Communication-protocol-based analysis and synthesis of networked systems: Progress, prospects and challenges. International Journal of Systems Science, 52(14), 3013–3034. https://doi.org/10.1080/00207721.2021.1917721

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.