Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A Variance-Constrained Method to Protocol-Based \(H_{\infty }\) State Estimation for Delayed Neural Networks with Randomly Occurring Sensor Nonlinearity

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

In this paper, we consider the \(H_{\infty }\) state estimation issue under variance constraint for recurrent neural networks with time-varying parameters, where the time-delay as well as randomly occurring sensor nonlinearity are handled and the communication is scheduled by round-robin protocol. The phenomenon of randomly occurring sensor nonlinearity is modeled by a random variable obeying the Bernoulli distribution with known probability. In order to alleviate unnecessary network congestion in communication channels, the round-robin protocol is introduced to specify which network node has the right to access the network channel at each time step. In particular, the objective is to develop the time-varying state estimation method such that, in the presence of time-delay, randomly occurring sensor nonlinearity and round-robin protocol, the sufficient conditions are given and both the error variance boundedness and the pre-set \(H_{\infty }\) performance index can be achieved simultaneously. In the end, we provide a simulation example with comparison tests to demonstrate the feasibility of presented \(H_{\infty }\) state estimation method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Benouar S, Hafid A, Kedir-Talha M, Seoane F (2021) Classification of impedance cardiography dz/dt complex subtypes using pattern recognition artificial neural networks. Biomed Eng Biomed Tech 66(5):515–527

    Article  Google Scholar 

  2. Lin A, Cheng J, Park JH, Yan H, Qi W (2023) Fault detection filtering of nonhomogeneous Markov switching memristive neural networks with output quantization. Inf Sci 632:715–729

    Article  Google Scholar 

  3. Wang M, Wang H, Zheng H (2022) A mini review of node centrality metrics in biological networks. Int J Netw Dyn Intell 1(1):99–110

    Google Scholar 

  4. Singh MP, Saraswat VK (2017) Multilayer feed forward neural networks for non-linear continuous bidirectional associative memory. Appl Soft Comput 61:700–713

    Article  Google Scholar 

  5. Onomi T, Maenami Y, Nakajima K (2011) Superconducting neural network for solving a combinatorial optimization problem. IEEE Trans Appl Supercond 21(3):701–704

    Article  Google Scholar 

  6. Li R, Gao X, Cao J (2019) Non-fragile state estimation for delayed fractional-order memristive neural networks. Appl Math Comput 340:221–233

    MathSciNet  Google Scholar 

  7. Li J, Dong H, Wang Z, Bu X (2020) Partial-neurons-based passivity-guaranteed state estimation for neural networks with randomly occurring time delays. IEEE Trans Neural Netw Learn Syst 31(9):3747–3753

    Article  MathSciNet  Google Scholar 

  8. Song X, Wu N, Song S, Stojanovic V (2023) Switching-like event-triggered state estimation for reaction-diffusion neural networks against DoS attacks. Neural Process Lett. https://doi.org/10.1007/s11063-023-11189-1

    Article  Google Scholar 

  9. Nagamani G, Shafiya M, Soundararajan G (2020) An LMI based state estimation for fractional-order memristive neural networks with leakage and time delays. Neural Process Lett 52(3):2089–2108

    Article  Google Scholar 

  10. Shen H, Xing M, Huo S, Wu Z-G, Park JH (2019) Finite-time \(H_{\infty }\) asynchronous state estimation for discrete-time fuzzy Markov jump neural networks with uncertain measurements. Fuzzy Sets Syst 356:113–128

    Article  Google Scholar 

  11. Tan G, Wang Z (2020) Further result on \(H_{\infty }\) performance state estimation of delayed static neural networks based on an improved reciprocally convex inequality. IEEE Trans Circuits Syst II Express Briefs 67(8):1477–1481

    Google Scholar 

  12. Feng S, Li X, Zhang S, Jian Z, Duan H, Wang Z (2023) A review: state estimation based on hybrid models of Kalman filter and neural network. Syst Sci Control Eng 11(1):Article No: 2173682. https://doi.org/10.1080/21642583.2023.2173682

  13. Sun K, Qiu J, Karimi HR, Gao H (2021) A novel finite-time control for nonstrict feedback saturated nonlinear systems with tracking error constraint. IEEE Trans Syst Man Cybern Syst 51(6):3968–3979

    Article  Google Scholar 

  14. Hu J, Wang Z, Liu G-P (2022) Delay compensation-based state estimation for time-varying complex networks with incomplete observations and dynamical bias. IEEE Trans Cybern 52(11):12071–12083

    Article  Google Scholar 

  15. Wang L, Wang Z, Wei G, Alsaadi FE (2019) Variance-constrained \(H_{\infty }\) state estimation for time-varying multi-rate systems with redundant channels: the finite-horizon case. Inf Sci 501:222–235

    Article  MathSciNet  Google Scholar 

  16. Hu J, Li J, Liu G-P, Yi X, Wu Z (2023) Optimized distributed filtering for time-varying saturated stochastic systems with energy harvesting sensors over sensor networks. IEEE Trans Signal Inf Process Netw 9:412–426

    MathSciNet  Google Scholar 

  17. Hu J, Wang C, Caballero-Águila R, Liu H (2023) Distributed optimal fusion filtering for singular systems with random transmission delays and packet dropout compensations. Commun Nonlinear Sci Numer Simul 119:Article No: 107093. https://doi.org/10.1016/j.cnsns.2023.107093

  18. Wang F, Wang Z, Liang J, Liu X (2019) Resilient state estimation for 2-D time-varying systems with redundant channels: a variance-constrained approach. IEEE Trans Cybern 49(7):2479–2489

    Article  Google Scholar 

  19. Gao Y, Hu J, Yu H, Du J, Jia C (2023) Variance-constrained resilient \(H_{\infty }\) state estimation for time-varying neural networks with random saturation observation under uncertain occurrence probability. Neural Process Lett 55(4):5031–5054

    Article  Google Scholar 

  20. Caballero-Águila R, Linares-Pérez J (2023) Distributed fusion filtering for uncertain systems with coupled noises, random delays and packet loss prediction compensation. Int J Syst Sci 54(2):371–390

    Article  MathSciNet  Google Scholar 

  21. Yu H, Yang W, Chen D, Wu Z (2023) \(H_{\infty }\) state-feedback control for saturated 2-D Roesser systems: the event-triggered case. Int J Syst Sci 54(1):59–72

    Article  MathSciNet  Google Scholar 

  22. Xu J, Niu Y, Lv X, Li W (2023) Sliding mode consensus control for multi-agent systems under component-based weighted try-once-discard protocol. Int J Syst Sci 54(12):2566–2578

    Article  MathSciNet  Google Scholar 

  23. Bao G, Ma L, Yi X (2022) Recent advances on cooperative control of heterogeneous multi-agent systems subject to constraints: a survey. Syst Sci Control Eng 10(1):539–551

    Article  Google Scholar 

  24. Li W, Yang F (2023) Information fusion over network dynamics with unknown correlations: an overview. Int J Netw Dyn Intell 2(2):Article Number: 100003. https://doi.org/10.53941/ijndi0201003

  25. Liu J, Wu Z-G, Yue D, Park JH (2021) Stabilization of networked control systems with hybrid-driven mechanism and probabilistic cyber attacks. IEEE Trans Syst Man Cybern Syst 51(2):943–953

    Article  Google Scholar 

  26. Wang Y, Shen B, Zou L, Han Q-L (2023) A survey on recent advances in distributed filtering over sensor networks subject to communication constraints. Int J Netw Dyn Intell 2(2):Article Number: 100007. https://doi.org/10.53941/ijndi0201007

  27. Wen P, Li X, Hou N, Mu S (2022) Distributed recursive fault estimation with binary encoding schemes over sensor networks. Syst Sci Control Eng 10(1):417–427

    Article  Google Scholar 

  28. Yao F, Ding Y, Hong S, Yang S-H (2022) A survey on evolved LoRa-based communication technologies for emerging internet of things applications. Int J Netw Dyn Intell 1(1):4–19

    Google Scholar 

  29. Wang X, Sun Y, Ding D (2022) Adaptive dynamic programming for networked control systems under communication constraints: a survey of trends and techniques. Int J Netw Dyn Intell 1(1):85–98

    Google Scholar 

  30. Wang Y, Shen B, Zou L, Han Q-L (2023) A survey on recent advances in distributed filtering over sensor networks subject to communication constraints. Int J Netw Dyn Intell 2:2. https://doi.org/10.53941/ijndi0201007

    Article  Google Scholar 

  31. Qiu J, Sun K, Wang T, Gao H (2019) Observer-based fuzzy adaptive event-triggered control for pure-feedback nonlinear systems with prescribed performance. IEEE Trans Fuzzy Syst 27(11):2152–2162

    Article  Google Scholar 

  32. Hu J, Hu Z, Caballero-Águila R, Chen C, Fan S, Yi X (2023) Distributed resilient fusion filtering for nonlinear systems with multiple missing measurements via dynamic event-triggered mechanism. Inf Sci 637:Article No:118950. https://doi.org/10.1016/j.ins.2023.118950

  33. Luo Y, Wang Z, Wei G, Alsaadi FE, Hayat T (2016) State estimation for a class of artificial neural networks with stochastically corrupted measurements under round-robin protocol. Neural Netw 77:70–79

    Article  Google Scholar 

  34. Qi L, Shi K, Yang C, Wen S (2021) Mean square stabilization of neural networks with weighted try once discard protocol and state observer. Neural Process Lett 53(1):829–842

    Article  Google Scholar 

  35. Alsaadi FE, 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

    Article  Google Scholar 

  36. Cheng J, Park JH, Chadli M (2022) Peak-to-peak fuzzy filtering of nonlinear discrete-time systems with Markov communication protocol. Inf Sci 607:361–376

    Article  Google Scholar 

  37. Cheng J, Park JH, Yan H, Wu Z-G (2022) An event-triggered round-robin protocol to dynamic output feedback control for nonhomogeneous Markov switching systems. Automatica 145:Article No. 110525. https://doi.org/10.1016/j.automatica.2022.110525

  38. Hu J, Li J, Yan H, Liu H (2023) Optimized distributed filtering for saturated systems with amplify-and-forward relays over sensor networks: a dynamic event-triggered approach. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2023.3308192

    Article  Google Scholar 

  39. Li J, Hu J, Cheng J, Wei Y, Yu H (2022) Distributed filtering for time-varying state-saturated systems with packet disorders: an event-triggered case. Appl Math Comput 434:Article No: 127411. https://doi.org/10.1016/j.amc.2022.127411

  40. Zou C, Li B, Du S, Chen X (2021) \(H_{\infty }\) state estimation for round-robin protocol-based Markovian jumping neural networks with mixed time delays. Neural Process Lett 53(6):4313–4330

    Article  Google Scholar 

  41. Shen Y, Wang Z, Shen B, Alsaadi FE, Dobaie AM (2020) \(\ell _{2}\)-\(\ell _{\infty }\) state estimation for delayed artificial neural networks under high-rate communication channels with round-robin protocol. Neural Netw 124:170–179

    Article  Google Scholar 

  42. Wang Y, Xu S, Li Y, Chu Y, Zhang Z (2021) Asynchronous finite-time state estimation for semi-Markovian jump neural networks with randomly occurred sensor nonlinearities. Neurocomputing 432:240–249

    Article  Google Scholar 

  43. Zha L, Fang J-A, Liu J, Tian E (2018) Event-triggered non-fragile state estimation for delayed neural networks with randomly occurring sensor nonlinearity. Neurocomputing 273:1–8

    Article  Google Scholar 

  44. Li J-N, Xu Y-F, Bao W-D, Li Z-J, Li L-S (2019) Finite-time non-fragile state estimation for discrete neural networks with sensor failures, time-varying delays and randomly occurring sensor nonlinearity. J Frankl Inst Eng Appl Math 356(3):1566–1589

    Article  MathSciNet  Google Scholar 

  45. Wang Y, Arumugam A, Liu Y, Alsaadi EF (2020) Finite-time event-triggered non-fragile state estimation for discrete-time delayed neural networks with randomly occurring sensor nonlinearity and energy constraints. Neurocomputing 384:115–129

    Article  Google Scholar 

  46. Rao H-X, Xu Y, Zhang B, Yao D (2018) Robust estimator design for periodic neural networks with polytopic uncertain weight matrices and randomly occurred sensor nonlinearities. IET Control Theory Appl 12(9):1299–1305

    Article  MathSciNet  Google Scholar 

  47. Wang F, Wang Z, Liang J, Liu X (2019) Resilient state estimation for 2-D time-varying systems with redundant channels: a variance-constrained approach. IEEE Trans Cybern 49(7):2479–2489

    Article  Google Scholar 

  48. Hu J, Wang Z, Liu G-P, Zhang H (2020) Variance-constrained recursive state estimation for time-varying complex networks with quantized measurements and uncertain inner coupling. IEEE Trans Neural Netw Learn Syst 31(6):1955–1967

    Article  MathSciNet  Google Scholar 

  49. Shen B, Wang Z, Shu H, Wei G (2011) \(H_{\infty }\) filtering for uncertain time-varying systems with multiple randomly occurred nonlinearities and successive packet dropouts. Int J Robust Nonlinear Control 21(14):1693–1709

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Applied Mathematics, Harbin University of Science and Technology, Harbin, 150080, China

    Yan Gao & Jun Hu

  2. Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems, Harbin University of Science and Technology, Harbin, 150080, China

    Yan Gao, Jun Hu, Hui Yu & Chaoqing Jia

  3. School of Automation, Harbin University of Science and Technology, Harbin, 150080, China

    Jun Hu, Cai Chen, Hui Yu & Chaoqing Jia

Authors
  1. Yan Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cai Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hui Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chaoqing Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YG and JH wrote the main manuscript text and prepared figures. CC did the simulation. HY and CJ checked the English. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Jun Hu or Cai Chen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported in part by the National Natural Science Foundation of China under Grant 12171124, the Natural Science Foundation of Heilongjiang Province of China under Grant ZD2022F003, and the Alexander von Humboldt Foundation of Germany.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, Y., Hu, J., Chen, C. et al. A Variance-Constrained Method to Protocol-Based \(H_{\infty }\) State Estimation for Delayed Neural Networks with Randomly Occurring Sensor Nonlinearity. Neural Process Lett 55, 12501–12523 (2023). https://doi.org/10.1007/s11063-023-11430-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-023-11430-x

Keywords

Search

Navigation