IJNDI/
Articles/
2509001357
  • Open Access
  • Article

Variance-constrained H∞ state estimation for time-varying delayed neural networks with random access protocol and sensor failures

  • Yan Gao 1, 2,   
  • Jun Hu 1, 2, 3, *,   
  • Hui Yu 2, 3,   
  • Chaoqing Jia 2, 3,   
  • Cai Chen 2, 3

Received: 03 Apr 2025 | Revised: 26 May 2025 | Published: 18 Sep 2025

Abstract

In this paper, a recursive state estimation method is proposed for delayed recurrent neural networks subject to random sensor failures and random access protocol, where time-varying characteristic and two performance evaluation indices are taken into account. The phenomenon of random sensor failures is characterized by introducing a random variable with certain occurrence probability. In order to prevent the data from collisions and save the resources, the random access protocol is introduced into the transmission channel, in which it is assumed that only one sensor node is allowed to access the network. Our aim is to propose the H∞ state estimation strategy without utilizing the augmentation method, where the estimator has the same order of the original neural state. In particular, we provide some sufficient conditions, which can guarantee two performance requirements from the noise attenuation and estimation accuracy perspectives. Finally, we use a simulation example to illustrate the feasibility of proposed H∞ state estimation method handling the concerned communication constraints.

References 

  • 1.
    Jiao, S.Y.; Shen, H.; Wei, Y.L.; et al. Further results on dissipativity and stability analysis of Markov jump generalized neural networks with time-varying interval delays. Appl. Math. Comput., 2018, 336: 338−350. doi: 10.1016/j.amc.2018.05.013
  • 2.
    Wang, M.Y.; Wang, H.Y.; Zheng, H. R. A mini review of node centrality metrics in biological networks. Int. J. Network Dyn. Intell., 2022, 1: 99−110. doi: 10.53941/ijndi0101009
  • 3.
    Wang, J.L.; Wu, H.Y.; Huang, T.W.; et al. Finite-time synchronization and H∞ synchronization for coupled neural networks with multistate or multiderivative couplings. IEEE Trans. Neural Networks Learning Syst., 2024, 35: 1628−1638. doi: 10.1109/TNNLS. 2022.3184487
  • 4.
    Wang, J.L.; Zhao, L.H. PD and PI control for passivity and synchronization of coupled neural networks with multi-weights. IEEE Trans. Network Sci. Eng., 2021, 8: 790−802. doi: 10.1109/TNSE.2021.3052889
  • 5.
    Chiu, K.S. Global exponential stability of bidirectional associative memory neural networks model with piecewise alternately advanced and retarded argument. Comp. Appl. Math., 2021, 40: 263. doi: 10.1007/s40314-021-01660-x
  • 6.
    Morocho-Cayamcela, M.E.; Lim, W. Pattern recognition of soldier uniforms with dilated convolutions and a modified encoder- decoder neural network architecture. Appl. Artif. Intell., 2021, 35: 476−487. doi: 10.1080/08839514.2021.1902124
  • 7.
    Cheng, J.; Wu, Y.Y.; Xiong, L.L.; et al. Resilient asynchronous state estimation of Markov switching neural networks: a hierarchical structure approach. Neural Networks, 2021, 135: 29−37. doi: 10.1016/j.neunet.2020.12.002
  • 8.
    Li, J.H.; Dong, H.L.; Wang, Z.D.; et al. Partial-neurons-based passivity-guaranteed state estimation for neural networks with randomly occurring time delays. IEEE Trans. Neural Networks Learning Syst., 2020, 31: 3747−3753. doi: 10.1109/TNNLS.2019. 2944552
  • 9.
    Qu, Y.; Pang, K. State estimation for a class of artificial neural networks subject to mixed attacks: a set-membership method. Neurocomputing, 2020, 411: 239−246. doi: 10.1016/j.neucom.2020.06.020
  • 10.
    Nagamani, G.; Shafiya, M.; Soundararajan, G. An LMI based state estimation for fractional-order memristive neural networks with leakage and time delays. Neural Process. Lett., 2020, 52: 2089−2108. doi: 10.1007/s11063-020- 10338-0
  • 11.
    Liu, S.; Wang, Z.D.; Shen, B.; et al. Partial-neurons-based state estimation for delayed neural networks with state-dependent noises under redundant channels. Inf. Sci., 2021, 547: 931−944. doi: 10.1016/j.ins.2020.08.047
  • 12.
    Tan, G.Q.; Wang, Z.S. Further result on H∞ performance state estimation of delayed static neural networks based on an improved reciprocally convex inequality. IEEE Trans. Circuits Syst. II: Express Br., 2020, 67: 1477−1481. doi: 10.1109/TCSII.2019.2941546
  • 13.
    Dinh, T.N.; Defoort, M. Fixed-time state estimation for a class of switched nonlinear time-varying systems. Asian J. Control, 2020, 22: 1782−1790. doi: 10.1002/asjc.2068
  • 14.
    Wang, L.C.; Wang, Z.D.; Wei, G.L.; et al. Variance-constrained H∞ state estimation for time-varying multi-rate systems with redundant channels: The finite-horizon case. Inf. Sci., 2019, 501: 222−235. doi: 10.1016/j.ins.2019.05.073
  • 15.
    Wang, F.; Wang, Z.D.; Liang, J.L.; et al. Resilient state estimation for 2-D time-varying systems with redundant channels: A variance-constrained approach. IEEE Trans. Cybern., 2019, 49: 2479−2489. doi: 10.1109/TCYB.2018.2821188
  • 16.
    Gao, Y.; Hu, J.; Yu, H.; et al. Variance-constrained resilient H∞ state estimation for time-varying neural networks with random saturation observation under uncertain occurrence probability. Neural Process. Lett., 2023, 55: 5031−5054. doi: 10.1007/s11063-022- 11078-z
  • 17.
    Gao, Y.; Hu, J.; Yu, H.; et al. Robust resilient H∞ state estimation for time-varying recurrent neural networks subject to probabilistic quantization under variance constraint. Int. J. Control Autom. Syst., 2023, 21: 684−695. doi: 10.1007/s12555-021-0676-x
  • 18.
    Wang, Y.C.; Wen, C.B.; Wu, X.B. Fault detection and isolation of floating wind turbine pitch system based on Kalman filter and multi-attention 1DCNN. Syst. Sci. Control Eng., 2024, 12: 2362169. doi: 10.1080/21642583.2024.2362169
  • 19.
    Caballero-Águila, R.; Hu, J.; Linares-Pérez, J. Filtering and smoothing estimation algorithms from uncertain nonlinear observations with time-correlated additive noise and random deception attacks. Int. J. Syst. Sci., 2024, 55: 2023−2035. doi: 10.1080/002077212024.2328781
  • 20.
    Pang, Z.H.; Fan, L.Z.; Sun, J.; et al. Detection of stealthy false data injection attacks against networked control systems via active data modification. Inf. Sci., 2021, 546: 192−205. doi: 10.1016/j.ins.2020.06.074
  • 21.
    Pang, Z.H.; Fan, L.Z.; Dong, Z.; et al. False data injection attacks against partial sensor measurements of networked control systems. IEEE Trans. Circuits Syst. II: Express Br., 2022, 69: 149−153. doi: 10.1109/TCSII.2021.3073724
  • 22.
    Yi, X.J.; Yu, H.Y.; Xu, T. Solving multi-objective weapon-target assignment considering reliability by improved MOEA/D-AM2M. Neurocomputing, 2024, 563: 126906. doi: 10.1016/j.neucom.2023.126906
  • 23.
    Cheng, J.; Park, J.H.; Wu, Z.G.; et al. Ultimate boundedness control for networked singularly perturbed systems with deception attacks: a Markovian communication protocol approach. IEEE Trans. Network Sci.Eng., 2022, 9: 445−456. doi: 10.1109/TNSE. 2021.3121414
  • 24.
    Song, H.Y.; Yao, H.Y.; Shi, P.; et al. Distributed secure state estimation of multi-sensor systems subject to two-channel hybrid attacks. IEEE Trans. Signal Inf. Process. Networks, 2022, 8: 1049−1058. doi: 10.1109/TSIPN.2023.3239681
  • 25.
    Zhang, B.; Rao, H.X.; Deng, Y.S.; et al. Finite horizon state estimation for time-varying neural networks with sensor failure and energy constraint. Neurocomputing, 2020, 372: 1−7. doi: 10.1016/j.neucom.2019.09.006
  • 26.
    Li, J.N.; Li, Z.J.; Xu, Y.F.; et al. Event-triggered non-fragile state estimation for discrete nonlinear Markov jump neural networks with sensor failures. Int. J. Control Autom. Syst., 2019, 17: 1131−1140. doi: 10.1007/s12555-018-0505-z
  • 27.
    Li, J.N.; Xu, Y.F.; Bao, W.D.; et al. Finite-time non-fragile state estimation for discrete neural networks with sensor failures, time- varying delays and randomly occurring sensor nonlinearity. J. Franklin Inst., 2019, 356: 1566−1589. doi: 10.1016/j.jfranklin.2018.10. 032
  • 28.
    Zhu, Q.X.; Huang, T. W. H∞ control of stochastic networked control systems with time-varying delays: The event-triggered sampling case. Int. J. Robust Nonlinear Control, 2021, 31: 9767−9781. doi: 10.1002/rnc.5798
  • 29.
    Yi, X.J.; Xu, T. Distributed event-triggered estimation for dynamic average consensus: A perturbation-injected privacy-preservation scheme. Inf. Fusion, 2024, 108: 102396. doi: 10.1016/j.inffus.2024.102396
  • 30.
    Li, Y.Y.; Liu, S.; Zhao, D.; et al. Event-triggered fault estimation for discrete time-varying systems subject to sector-bounded nonlinearity: A Krein space based approach. Int. J. Robust Nonlinear Control, 2021, 31: 5360−5380. doi: 10.1002/rnc.5545
  • 31.
    Li, C.Y.; Liu, Y.F.; Gao, M.; et al. Fault-tolerant formation consensus control for time-varying multi-agent systems with stochastic communication protocol. Int. J. Network Dyn. Intell., 2024, 3: 100004. doi: 10.53941/ijndi.2024.100004
  • 32.
    Wang, W.; Wang, M. Adaptive neural event-triggered output-feedback optimal tracking control for discrete-time pure-feedback nonlinear systems. Int. J. Network Dyn. Intell., 2024, 3: 100010. doi: 10.53941/ijndi.2024.100010
  • 33.
    Zhang, R.; Liu, H.J.; Liu, Y.F.; et al. Dynamic event-triggered state estimation for discrete-time delayed switched neural networks with constrained bit rate. Syst. Sci. Control Eng., 2024, 12: 2334304. doi: 10.1080/21642583.2024.2334304
  • 34.
    Cui, G.F.; Wu, L.B.; Wu, M. Adaptive event-triggered fault-tolerant control for leader-following consensus of multi-agent systems. Int. J. Syst. Sci., 2024, 55: 3291−3303. doi: 10.1080/00207721.2024.2367074
  • 35.
    Cong, G.; Han, F.; Li, J.H.; et al. Event-triggered distributed filtering for discrete-time systems with integral measurements. Syst. Sci. Control Eng., 2021, 9: 272−282. doi: 10.1080/21642583.2021.1901157
  • 36.
    Sun, Y.; Tian, X.; Wei, G.L. Finite-time distributed resilient state estimation subject to hybrid cyber-attacks: A new dynamic event triggered case. Int. J. Syst. Sci., 2022, 53: 2832−2844. doi: 10.1080/00207721.2022.2083256
  • 37.
    Song, H.Y.; Zhang, W.A.; Yu, L.; et al. Multisensor-based periodic estimation in sensor networks with transmission constraint and periodic mixed storage. IEEE Trans. Cybern., 2017, 47: 4367−4379. doi: 10.1109/TCYB.2016.2609503
  • 38.
    Meng, X.Y.; Chen, Y.; Ma, L.F.; et al. Protocol-based variance-constrained distributed secure filtering with measurement censoring. Int. J. Syst. Sci., 2022, 53: 3322−3338. doi: 10.1080/00207721.2022.2080297
  • 39.
    Liu, H.J.; Wang, Z.D.; Fei, W. Y.; et al. H∞ and ℓ2-ℓ∞ state estimation for delayed memristive neural networks on finite horizon: The Round-Robin protocol. Neural Networks, 2020, 132: 121−130. doi: 10.1016/j.neunet.2020.08.006
  • 40.
    Qi, L.X.; Shi, K.B.; Yang, C.D.; et al. Mean square stabilization of neural networks with weighted try once discard protocol and state observer. Neural Process. Lett., 2021, 53: 829−842. doi: 10.1007/s11063-020- 10409-2
  • 41.
    Zhang, H.X.; Hu, J.; Liu, H.J.; et al. Recursive state estimation for time-varying complex networks subject to missing measurements and stochastic inner coupling under random access protocol. Neurocomputing, 2019, 346: 48−57. doi: 10.1016/j.neucom.2018.07. 086
  • 42.
    Alsaadi, F.E.; Wang, Z.D.; Luo, Y. Q.; et al. H∞ state estimation for BAM neural networks with binary mode switching and distributed leakage delays under periodic scheduling protocol. IEEE Trans. Neural Networks Learning Syst., 2022, 33: 4160−4172. doi: 10.1109/TNNLS.2021.3055942
  • 43.

    Zou, C.; Li, B.; Du, S. S.; et al. H∞ state estimation for round-robin protocol-based Markovian jumping neural networks with mixed time delays. Neural Process. Lett., 2021, 53: 4313−4330. doi: 10.1007/s11063-021- 10598-4

  • 44.
    Liu, H.J.; Wang, Z.D.; Fei, W.Y.; et al. On finite-horizon H∞ state estimation for discrete-time delayed memristive neural networks under stochastic communication protocol. Inf. Sci., 2021, 555: 280−292. doi: 10.1016/j.ins.2020.11.002
  • 45.
    Tabbara, M.; Nesic, D. Input-output stability of networked control systems with stochastic protocols and channels. IEEE Trans. Automat. Control, 2008, 53: 1160−1175. doi: 10.1109/TAC.2008.923691
Share this article:
Download PDF
DOI
10.53941/ijndi.2025.100019
Issue
Volume 4, Issue 3
How to Cite
Gao, Y.; Hu, J.; Yu, H.; Jia, C.; Chen, C. Variance-constrained H∞ state estimation for time-varying delayed neural networks with random access protocol and sensor failures. International Journal of Network Dynamics and Intelligence 2025, 4 (3), 100019. https://doi.org/10.53941/ijndi.2025.100019.
RIS
BibTex
Copyright & License
article copyright Image
Copyright (c) 2025 by the authors.