Dynamic Event-triggered H∞ State Estimation for Memristive Neural Networks with Variance Constraints and Time-delay: A Finite-horizon Approach
DOI:
https://doi.org/10.15377/2409-5761.2025.12.10Keywords:
Time-delay system, H∞ state estimation, Memristive neural networks, Resource-efficient estimation, Variance-constrained estimation, Dynamic event-triggered mechanismAbstract
This paper discusses the dynamic event-triggered H∞ state estimation issue for memristive neural networks with time-delay under variance constraints. The dynamic event-triggered mechanism is incorporated into the sensor-to-estimator to reduce resource consumption in the communication channel. The objective is to design the time-varying state estimator such that, in the presence of the dynamic event-triggered mechanism and time-delay, new sufficient criteria are derived to ensure the desired H∞ performance and the boundedness of estimation error variance. Furthermore, a novel non-augmented H∞ state estimation algorithm is proposed under variance constraint by using the stochastic analysis techniques. Finally, a simulation example is used to illustrate the effectiveness of the proposed H∞ state estimation algorithm.
Downloads
References
Yao W, Gao K, Wang Y, Lin HR, Wu HW, Xu C, et al. Generalization and differentiation of affective associative memory circuit based on memristive neural network with emotion transfer. Neural Netw. 2025; 188: 107502. https://doi.org/10.1016/j.neunet.2025.107502 DOI: https://doi.org/10.1016/j.neunet.2025.107502
Liu N, Jia WW, Qin ST. A smooth gradient approximation neural network for general constrained nonsmooth nonconvex optimization problems. Neural Netw. 2025; 184: 107121. https://doi.org/10.1016/j.neunet.2024.107121 DOI: https://doi.org/10.1016/j.neunet.2024.107121
Peng C, Xu CC, Kudryavtsev F, Ai Q, Gao YJ, Jiao YL. Spatiotemporal factorized graph neural networks for joint large-scale traffic prediction and online pattern recognition. IEEE Trans Intell Transp Syst. 2025; 26(10): 14896-909. https://doi.org/10.1109/TITS.2025.3585197 DOI: https://doi.org/10.1109/TITS.2025.3585197
Chua LO. Memristor-the missing circuit element. IEEE Trans Circuit Theory. 1971; 18(5): 507-19. https://doi.org/10.1109/TCT.1971.1083337 DOI: https://doi.org/10.1109/TCT.1971.1083337
Strukov DB, Snider GS, Stewart DR, Williams RS. The missing memristor found. Nature. 2008; 453(7191): 80-3. https://doi.org/10.1038/nature06932 DOI: https://doi.org/10.1038/nature06932
Li XY, Fang JA, Li HY. Exponential synchronization of stochastic memristive recurrent neural networks under alternate state feedback control. Int J Control Autom Syst. 2018; 16(6): 2859-69. https://doi.org/10.1007/s12555-018-0225-4 DOI: https://doi.org/10.1007/s12555-018-0225-4
Fu QH, Cai JY, Zhong SM, Yu YB. Pinning impulsive synchronization of stochastic memristor-based neural networks with time-varying delays. Int J Control Autom Syst. 2019; 17(1): 243-52. https://doi.org/10.1007/s12555-018-0295-3 DOI: https://doi.org/10.1007/s12555-018-0295-3
Zhirnov VV, Meade R, Cavin RK, Sandhu G. Scaling limits of resistive memories. Nanotechnology. 2011; 22 (25): 254027. https://doi.org/10.1088/0957-4484/22/25/254027 DOI: https://doi.org/10.1088/0957-4484/22/25/254027
Liu J, Li TS, Duan SK, Wang LD. Energy consumption analysis for the read and write mode of the memristor with voltage threshold in the real-time control system. Neurocomputing. 2017; 266: 477-84. https://doi.org/10.1016/j.neucom.2017.05.062 DOI: https://doi.org/10.1016/j.neucom.2017.05.062
Ho YP, Huang GM, Li P. Dynamical properties and design analysis for nonvolatile memristor memories. IEEE Trans Circuits Syst I-Regul Pap. 2011; 58(4): 724-36. https://doi.org/10.1109/TCSI.2010.2078710 DOI: https://doi.org/10.1109/TCSI.2010.2078710
Li RX, Cao JD. Exponential state estimation and passivity of fuzzy quaternion-valued memristive neural networks: Norm approach. IEEE Trans Syst Man Cybern Syst. 2024; 54(8): 4798-805. https://doi.org/10.1109/TSMC.2024.3387412 DOI: https://doi.org/10.1109/TSMC.2024.3387412
Zha LJ, Miao JZ, Liu JL, Xie XP, Tian EG. State estimation for delayed memristive neural networks with multichannel round-robin protocol and multimodal injection attacks. IEEE Trans Syst Man Cybern. Syst. 2024; 54(6): 3738-48. https://doi.org/10.1109/TSMC.2024.3370221 DOI: https://doi.org/10.1109/TSMC.2024.3370221
Liu YF, Shen B, Liu HJ, Huang TW, Tan HL, Sun J. Dynamic event-triggered H∞ state estimation for discrete-time complex-valued memristive neural networks with mixed time delays. Neural Netw. 2025; 190: 107631. https://doi.org/10.1016/j.neunet.2025.107631 DOI: https://doi.org/10.1016/j.neunet.2025.107631
Shao XG, Zhang J, Lyu M, Lu YJ. Event-based nonfragile state estimation for memristive neural networks with multiple time-delays and sensor saturations. Int J Syst Sci. 2025; 56(3): 618-37. https://doi.org/10.1080/00207721.2024.2408529 DOI: https://doi.org/10.1080/00207721.2024.2408529
Tang TF, Qin G, Zhang B, Cheng J, Cao JD. Event-based asynchronous state estimation for Markov jump memristive neural networks. Appl Math Comput. 2024; 473: 128653. https://doi.org/10.1016/j.amc.2024.128653 DOI: https://doi.org/10.1016/j.amc.2024.128653
Xu BR, Hu XF, Li SL. Secure state estimation of memristive neural networks with dynamic self-triggered strategy subject to deception attacks. Neurocomputing. 2024; 601: 128142. https://doi.org/10.1016/j.neucom.2024.128142 DOI: https://doi.org/10.1016/j.neucom.2024.128142
Shao XG, Zhang J, Lu YJ. Event-based nonfragile state estimation for memristive recurrent neural networks with stochastic cyber-attacks and sensor saturations. Chin Phys B. 2024; 33(7): 070203. https://doi.org/10.1088/1674-1056/ad3dcb DOI: https://doi.org/10.1088/1674-1056/ad3dcb
Zhang R, Liu HJ, Liu YF, Tan HL. Dynamic event-triggered state estimation for discrete-time delayed switched neural networks with constrained bit rate. Syst Sci Control Eng. 2024; 12(1): Article: 2334304. https://doi.org/10.1080/21642583.2024.2334304 DOI: https://doi.org/10.1080/21642583.2024.2334304
Liu HJ, Wang ZD, Fei WY, Li JH, Alsaadi FE. On finite-horizon H∞ state estimation for discrete-time delayed memristive neural networks under stochastic communication protocol. Inf Sci. 2021; 555: 280-92. https://doi.org/10.1016/j.ins.2020.11.002 DOI: https://doi.org/10.1016/j.ins.2020.11.002
Zhu S, Wang LD, Duan SK. Memristive pulse coupled neural network with applications in medical image processing. Neurocomputing. 2017; 227: 149-57. https://doi.org/10.1016/j.neucom.2016.07.068 DOI: https://doi.org/10.1016/j.neucom.2016.07.068
Wang WP, Yu X, Luo X, Li LX. Stability analysis of memristive multidirectional associative memory neural networks and applications in information storage. Mod Phys Lett B. 2018; 32(18): 1850207. https://doi.org/10.1142/S021798491850207X DOI: https://doi.org/10.1142/S021798491850207X
Wang WP, Jia X, Luo X, Kurths J, Yuan MM. Fixed-time synchronization control of memristive MAM neural networks with mixed delays and application in chaotic secure communication. Chaos Solitons Fractals. 2019; 126: 85-96. https://doi.org/10.1016/j.chaos.2019.05.041 DOI: https://doi.org/10.1016/j.chaos.2019.05.041
Li Q, Shen B, Wang ZD, Huang TW, Luo J. Synchronization control for a class of discrete time-delay complex dynamical networks: A dynamic event-triggered approach. IEEE Trans Cybern. 2019; 49(5): 1979-86. https://doi.org/10.1109/TCYB.2018.2818941
Guo JY, Wang ZD, Zou L, Dong HL. Finite-horizon H∞ state estimation for discrete time-varying artificial neural networks: An accumulation-based event-triggered mechanism. IEEE Trans Netw Sci Eng. 2022; 9(6): 4184-97. https://doi.org/10.1109/TNSE.2022.3196306 DOI: https://doi.org/10.1109/TNSE.2022.3196306
Zou C, Li B, Liu FY, Xu BR. Event-triggered mu -state estimation for Markovian jumping neural networks with mixed time-delays. Appl Math Comput. 2022; 425: 127056. https://doi.org/10.1016/j.amc.2022.127056 DOI: https://doi.org/10.1016/j.amc.2022.127056
Tao J, Xiao ZH, Li ZY, Wu J, Lu RQ, Shi P, et al. Dynamic event-triggered state estimation for Markov jump neural networks with partially unknown probabilities. IEEE Trans Neural Netw Learn Syst. 2022; 33(12): 7438-47. https://doi.org/10.1109/TNNLS.2021.3085001 DOI: https://doi.org/10.1109/TNNLS.2021.3085001
Liu YF, Shen B, Shu HS. Finite-time resilient H∞ state estimation for discrete-time delayed neural networks under dynamic event-triggered mechanism. Neural Netw. 2020; 121: 356-65. https://doi.org/10.1016/j.neunet.2019.09.006 DOI: https://doi.org/10.1016/j.neunet.2019.09.006
Li Q, Shen B, Wang ZD, Sheng WG. Recursive distributed filtering over sensor networks on Gilbert-Elliott channels: A dynamic event-triggered approach. Automatica. 2020; 113: 108681. https://doi.org/10.1016/j.automatica.2019.108681 DOI: https://doi.org/10.1016/j.automatica.2019.108681
Deng J, Li HL, Hu C, Jiang HJ, Cao JD. State estimation of discrete-time fractional-order nonautonomous neural networks with time delays. IEEE Trans Syst Man Cybern Syst. 2025; 55(5): 3707-19. https://doi.org/10.1109/TSMC.2025.3546945 DOI: https://doi.org/10.1109/TSMC.2025.3546945
Sun ZL, Han CY. Linear state estimation for multi-rate NCSs with multi-channel observation delays and unknown Markov packet losses. Int J Netw Dyn Intell. 2025; 4: 100005. https://doi.org/10.53941/ijndi.2025.100005. DOI: https://doi.org/10.53941/ijndi.2025.100005
Wang T, Zhang BY. Mixed H∞/L2-L∞ state estimation for delayed memristive neural networks with Markov switching parameters. Circuits Syst Signal Process. 2024; 43(8): 4869-90. https://doi.org/10.1007/s00034-024-02711-4 DOI: https://doi.org/10.1007/s00034-024-02711-4
Wang T, Zhang BY, Yuan DM, Zhang YJ. Event-based extended dissipative state estimation for memristor-based Markovian neural networks with hybrid time-varying delays. IEEE Trans Circuits Syst I-Regul Pap. 2021; 68(11): 4520-33. https://doi.org/10.1109/TCSI.2021.3077485 DOI: https://doi.org/10.1109/TCSI.2021.3077485
Qian W, Xing WW, Fei SM. H∞ state estimation for neural networks with general activation function and mixed time-varying delays. IEEE Trans Neural Netw Learn Syst. 2021; 32(9): 3909-18. https://doi.org/10.1109/TNNLS.2020.3016120 DOI: https://doi.org/10.1109/TNNLS.2020.3016120
Wang ZY, Wang PD, Wang JS, Lou P, Li L. State estimation for measurement-saturated memristive neural networks with missing measurements and mixed time delays subject to cyber-attacks: A non-fragile set-membership filtering framework. Appl Sci Basel. 2024; 14(19): 8936. https://doi.org/10.3390/app14198936 DOI: https://doi.org/10.3390/app14198936
Rajchakit G, Banu KA, Aparna T, Lim CP. Event-triggered secure control for Markov jump neural networks with time-varying delays and subject to cyber-attacks via state estimation fuzzy approach. Int J Syst Sci. 2025; 56(2): 211-26. https://doi.org/10.1080/00207721.2024.2390694 DOI: https://doi.org/10.1080/00207721.2024.2390694
Zhao D, Wang ZD, Wei GL, Liu XH. Nonfragile H∞ state estimation for recurrent neural networks with time-varying delays: On proportional-integral observer design. IEEE Trans Neural Netw Learn Syst. 2021; 32(8): 3553-65. https://doi.org/10.1109/TNNLS.2020.3015376 DOI: https://doi.org/10.1109/TNNLS.2020.3015376
Wang LC, Wang ZD, Wei GL, Alsaadi FE. Variance-constrained H∞ state estimation for time-varying multi-rate systems with redundant channels: The finite-horizon case. Inf Sci. 2019; 501: 222-35. https://doi.org/10.1016/j.ins.2019.05.073 DOI: https://doi.org/10.1016/j.ins.2019.05.073
Gao Y, Hu J, Yu H, Du JH, Jia CQ. Variance-constrained resilient H∞ state estimation for time-varying neural networks with random saturation observation under uncertain occurrence probability. Neural Process Lett. 2023; 55(4): 5031-54. https://doi.org/10.1007/s11063-022-11078-z DOI: https://doi.org/10.1007/s11063-022-11078-z
Wu AL, Zeng ZG. Anti-synchronization control of a class of memristive recurrent neural networks. Commun Nonlinear Sci Numer Simul. 2013; 18(2): 373-85. https://doi.org/10.1016/j.cnsns.2012.07.005 DOI: https://doi.org/10.1016/j.cnsns.2012.07.005
Shen B, Wang ZD, Shu HS, Wei GL. H∞ filtering for uncertain time-varying systems with multiple randomly occurred nonlinearities and successive packet dropouts. Int J Robust Nonlinear Control. 2011; 21(14): 1693-709. https://doi.org/10.1002/rnc.1662 DOI: https://doi.org/10.1002/rnc.1662
Li Q, Shen B, Wang ZD, Huang TW, Luo J. Synchronization control for a class of discrete time-delay complex dynamical networks: A dynamic event-triggered approach. IEEE Trans Cybern. 2019; 49(5): 1979-86. https://doi.org/10.1109/TCYB.2018.2818941 DOI: https://doi.org/10.1109/TCYB.2018.2818941
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Yan Gao, Yan Zhang, Jun Hu
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All the published articles are licensed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
