IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v316y2018icp205-214.html
   My bibliography  Save this article

Network-based H∞ state estimation for neural networks using imperfect measurement

Author

Listed:
  • Lee, Tae H.
  • Park, Ju H.
  • Jung, Hoyoul

Abstract

This study considers the network-based H∞ state estimation problem for neural networks where transmitted measurements suffer from the sampling effect, external disturbance, network-induced delay, and packet dropout as network constraints. The external disturbance, network-induced delay, and packet dropout affect the measurements at only the sampling instants owing to the sampling effect. In addition, when packet dropout occurs, the last received data are used. To tackle the imperfect signals, a compensator is designed, and then by aid of the compensator, H∞ filter which guarantees desired performance is designed as well. A numerical example is given to illustrate the validity of the proposed methods.

Suggested Citation

  • Lee, Tae H. & Park, Ju H. & Jung, Hoyoul, 2018. "Network-based H∞ state estimation for neural networks using imperfect measurement," Applied Mathematics and Computation, Elsevier, vol. 316(C), pages 205-214.
    • Handle: RePEc:eee:apmaco:v:316:y:2018:i:c:p:205-214
    DOI: 10.1016/j.amc.2017.08.034
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300317305854
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2017.08.034?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fister, Iztok & Ljubič, Karin & Suganthan, Ponnuthurai Nagaratnam & Perc, Matjaž & Fister, Iztok, 2015. "Computational intelligence in sports: Challenges and opportunities within a new research domain," Applied Mathematics and Computation, Elsevier, vol. 262(C), pages 178-186.
    2. Shen, Mouquan & Ye, Dan, 2017. "A finite frequency approach to control of Markov jump linear systems with incomplete transition probabilities," Applied Mathematics and Computation, Elsevier, vol. 295(C), pages 53-64.
    3. Li, Feng & Shen, Hao & Chen, Mengshen & Kong, Qingkai, 2015. "Non-fragile finite-time l2−l∞ state estimation for discrete-time Markov jump neural networks with unreliable communication links," Applied Mathematics and Computation, Elsevier, vol. 271(C), pages 467-481.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Jun & Shi, Kaibo & Huang, Qinzhen & Zhong, Shouming & Zhang, Dian, 2018. "Stochastic switched sampled-data control for synchronization of delayed chaotic neural networks with packet dropout," Applied Mathematics and Computation, Elsevier, vol. 335(C), pages 211-230.
    2. Li, Yueyang & Song, Xinmin & Zhang, Zhijie & Zhao, Dong & Wang, Zhonghua, 2019. "H∞ deconvolution filter design for uncertain linear discrete time-variant systems: A Krein space approach," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 131-143.
    3. Jiao, Shiyu & Shen, Hao & Wei, Yunliang & Huang, Xia & Wang, Zhen, 2018. "Further results on dissipativity and stability analysis of Markov jump generalized neural networks with time-varying interval delays," Applied Mathematics and Computation, Elsevier, vol. 336(C), pages 338-350.
    4. Wang, Jing & Hu, Xiaohui & Wei, Yunliang & Wang, Zhen, 2019. "Sampled-data synchronization of semi-Markov jump complex dynamical networks subject to generalized dissipativity property," Applied Mathematics and Computation, Elsevier, vol. 346(C), pages 853-864.
    5. Xiong, Jun & Chang, Xiao-Heng & Yi, Xiaojian, 2018. "Design of robust nonfragile fault detection filter for uncertain dynamic systems with quantization," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 774-788.
    6. Fang, Tian & Jiao, Shiyu & Fu, Dongmei & Su, Lei, 2021. "Passivity-based synchronization for Markov switched neural networks with time delays and the inertial term," Applied Mathematics and Computation, Elsevier, vol. 394(C).
    7. Zheng, Wei & Zhang, Zhiming & Sun, Fuchun & Lam, Hak Keung & Wen, Shuhuan, 2022. "Stability analysis and robust controller design for systems with mixed time-delays and stochastic nonlinearity via cone complementarity linearization," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    8. Zeng, Hong-Bing & Zhai, Zheng-Liang & He, Yong & Teo, Kok-Lay & Wang, Wei, 2020. "New insights on stability of sampled-data systems with time-delay," Applied Mathematics and Computation, Elsevier, vol. 374(C).
    9. Tan, Guoqiang & Wang, Zhanshan & Li, Cong, 2020. "H∞ performance state estimation of delayed static neural networks based on an improved proportional-integral estimator," Applied Mathematics and Computation, Elsevier, vol. 370(C).
    10. Yang, Haijiao & Ye, Dan, 2020. "Time-varying formation tracking control for high-order nonlinear multi-agent systems in fixed-time framework," Applied Mathematics and Computation, Elsevier, vol. 377(C).
    11. Zhou, Yu & Pan, Yingnan & Li, Shubo & Liang, Hongjing, 2020. "Event-triggered cooperative containment control for a class of uncertain non-identical networks," Applied Mathematics and Computation, Elsevier, vol. 375(C).
    12. Malik, Saddam Hussain & Tufail, Muhammad & Rehan, Muhammad & Ahmed, Shakeel, 2022. "State and output feedback local control schemes for nonlinear discrete-time 2-D Roesser systems under saturation, quantization and slope restricted input," Applied Mathematics and Computation, Elsevier, vol. 423(C).
    13. Fu, Haijing & Li, Jiahui & Han, Fei & Hou, Nan & Dong, Hongli, 2021. "Outlier-resistant bserver-based H∞ PID control under stochastic communication protocol," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    14. Li, Xin & Wei, Guoliang & Ding, Derui, 2021. "Distributed resilient interval estimation for sensor networks under aperiodic denial-of-service attacks and adaptive event-triggered protocols," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    15. Hu, Jun & Li, Jiaxing & Kao, Yonggui & Chen, Dongyan, 2022. "Optimal distributed filtering for nonlinear saturated systems with random access protocol and missing measurements: The uncertain probabilities case," Applied Mathematics and Computation, Elsevier, vol. 418(C).
    16. Wang, Jing & Liang, Kun & Huang, Xia & Wang, Zhen & Shen, Hao, 2018. "Dissipative fault-tolerant control for nonlinear singular perturbed systems with Markov jumping parameters based on slow state feedback," Applied Mathematics and Computation, Elsevier, vol. 328(C), pages 247-262.
    17. Shi, Xuanxuan & Shen, Mouquan, 2019. "A new approach to feedback feed-forward iterative learning control with random packet dropouts," Applied Mathematics and Computation, Elsevier, vol. 348(C), pages 399-412.
    18. Hao, Li-Ying & Yu, Ying & Li, Hui, 2019. "Fault tolerant control of UMV based on sliding mode output feedback," Applied Mathematics and Computation, Elsevier, vol. 359(C), pages 433-455.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gao, Xianwen & He, Hangfeng & Qi, Wenhai, 2017. "Admissibility analysis for discrete-time singular Markov jump systems with asynchronous switching," Applied Mathematics and Computation, Elsevier, vol. 313(C), pages 431-441.
    2. Jiang, Tingting & Zhang, Yuping & Zeng, Yong & Zhong, Shouming & Shi, Kaibo & Cai, Xiao, 2021. "Finite-time analysis for networked predictive control systems with induced time delays and data packet dropouts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    3. Luo, Jinnan & Tian, Wenhong & Zhong, Shouming & Shi, Kaibo & Chen, Hao & Gu, Xian-Ming & Wang, Wenqin, 2017. "Non-fragile asynchronous H∞ control for uncertain stochastic memory systems with Bernoulli distribution," Applied Mathematics and Computation, Elsevier, vol. 312(C), pages 109-128.
    4. Liang, Kun & Dai, Mingcheng & Shen, Hao & Wang, Jing & Wang, Zhen & Chen, Bo, 2018. "L2−L∞ synchronization for singularly perturbed complex networks with semi-Markov jump topology," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 450-462.
    5. Fang, Liandi & Ma, Li & Ding, Shihong & Zhao, Dean, 2019. "Finite-time stabilization for a class of high-order stochastic nonlinear systems with an output constraint," Applied Mathematics and Computation, Elsevier, vol. 358(C), pages 63-79.
    6. Jiao, Shiyu & Shen, Hao & Wei, Yunliang & Huang, Xia & Wang, Zhen, 2018. "Further results on dissipativity and stability analysis of Markov jump generalized neural networks with time-varying interval delays," Applied Mathematics and Computation, Elsevier, vol. 336(C), pages 338-350.
    7. Kostić, Srđan & Stojković, Milan & Prohaska, Stevan, 2016. "Hydrological flow rate estimation using artificial neural networks: Model development and potential applications," Applied Mathematics and Computation, Elsevier, vol. 291(C), pages 373-385.
    8. Zhai, Ding & Lu, An-Yang & Dong, Jiuxiang & Zhang, Qing-Ling, 2016. "Asynchronous H∞ filtering for 2D discrete Markovian jump systems with sensor failure," Applied Mathematics and Computation, Elsevier, vol. 289(C), pages 60-79.
    9. Aravindh, D. & Sakthivel, R. & Kong, Fanchao & Marshal Anthoni, S., 2020. "Finite-time reliable stabilization of uncertain semi-Markovian jump systems with input saturation," Applied Mathematics and Computation, Elsevier, vol. 384(C).
    10. Wu, Kai-Ning & Sun, Han-Xiao & Yang, Baoqing & Lim, Cheng-Chew, 2018. "Finite-time boundary control for delay reaction–diffusion systems," Applied Mathematics and Computation, Elsevier, vol. 329(C), pages 52-63.
    11. Abdullah Erdal Tümer & Sabri Koçer, 2017. "Prediction of team league’s rankings in volleyball by artificial neural network method," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 17(3), pages 202-211, May.
    12. Li, Xin & Wei, Guoliang & Ding, Derui, 2021. "Distributed resilient interval estimation for sensor networks under aperiodic denial-of-service attacks and adaptive event-triggered protocols," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    13. Song, Xiaona & Wang, Mi & Song, Shuai & Wang, Zhen, 2019. "Quantized output feedback control for nonlinear Markovian jump distributed parameter systems with unreliable communication links," Applied Mathematics and Computation, Elsevier, vol. 353(C), pages 371-395.
    14. Gao, Rui & Zhai, Ding & Cheng, Jun, 2019. "Decentralized static output feedback sliding mode control for interconnected descriptor systems via linear sliding variable," Applied Mathematics and Computation, Elsevier, vol. 357(C), pages 185-198.
    15. Shen, Mouquan & Ye, Dan, 2017. "A finite frequency approach to control of Markov jump linear systems with incomplete transition probabilities," Applied Mathematics and Computation, Elsevier, vol. 295(C), pages 53-64.
    16. Devansh Patel & Dhwanil Shah & Manan Shah, 2020. "The Intertwine of Brain and Body: A Quantitative Analysis on How Big Data Influences the System of Sports," Annals of Data Science, Springer, vol. 7(1), pages 1-16, March.
    17. Alessandro Innocenti & Tommaso Nannicini & Roberto Ricciuti, 2021. "The Importance of Betting Early," Risks, MDPI, vol. 9(4), pages 1-15, April.
    18. Li, Feng & Song, Shuai & Zhao, Jianrong & Xu, Shengyuan & Zhang, Zhengqiang, 2019. "Synchronization control for Markov jump neural networks subject to HMM observation and partially known detection probabilities," Applied Mathematics and Computation, Elsevier, vol. 360(C), pages 1-13.
    19. Erkaymaz, Okan & Ozer, Mahmut & Perc, Matjaž, 2017. "Performance of small-world feedforward neural networks for the diagnosis of diabetes," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 22-28.
    20. Song, Xiaona & Men, Yunzhe & Zhou, Jianping & Zhao, Junjie & Shen, Hao, 2017. "Event-triggered H∞ control for networked discrete-time Markov jump systems with repeated scalar nonlinearities," Applied Mathematics and Computation, Elsevier, vol. 298(C), pages 123-132.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:316:y:2018:i:c:p:205-214. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.