Network-Based \(\mathscr {H}_{\infty }\) State Estimation for Neural Networks Using Limited Measurement

  • Ju H. Park
  • Hao Shen
  • Xiao-Heng Chang
  • Tae H. Lee
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 170)


This chapter is concerned with the network-based \(\mathscr {H}_{\infty }\) state estimation problem for neural networks. Because of network constraints, we consider that transmitted measurements suffer from the sampling effect, external disturbance, network-induced delay, and packet dropout, simultaneously. 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 overcome the difficulty in estimating original signals from the limited signals, a compensator is designed. By aid of the compensator, a state estimator designed which guarantees desired \(\mathscr {H}_{\infty }\) performance. A numerical example is given to illustrate the validity of the proposed methods.


Neural network State estimation \(\mathscr {H}_{\infty }\) control Sampling Transmission delay Packet dropout. 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Ju H. Park
    • 1
  • Hao Shen
    • 2
  • Xiao-Heng Chang
    • 3
  • Tae H. Lee
    • 4
  1. 1.Department of Electrical EngineeringYeungnam UniversityKyongsanKorea (Republic of)
  2. 2.School of Electrical and Information EngineeringAnhui University of TechnologyMa’anshanChina
  3. 3.School of Information Science and EngineeringWuhan University of Science and TechnologyWuhanChina
  4. 4.Division of Electronic EngineeringChonbuk National UniversityJeonjuKorea (Republic of)

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