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

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  • 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
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    1. 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.
    2. 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.
    3. 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.
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