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Finite Time State Estimation of Complex-valued BAM Neutral-type Neural Networks with Time-varying Delays

  • Runan Guo
  • Ziye ZhangEmail author
  • Chong Lin
  • Yuming Chu
  • Yongmin Li
Article
  • 13 Downloads

Abstract

This paper considers the finite time state estimation problem of complex-valued bidirectional associative memory (BAM) neutral-type neural networks with time-varying delays. By resorting to the Lyapunov function approach, the Wirtinger inequality and the reciprocally convex approach, a delay-dependent criterion in terms of LMIs is established to guarantee the finite-time boundedness of the error-state system for the addressed system. Meanwhile, an effective state estimator is designed to estimate the network states through the available output measurements. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed results.

Keywords

Complex-valued BAM neural networks finite time state estimation neutral-type neural networks timevarying delays 

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Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Runan Guo
    • 1
    • 2
  • Ziye Zhang
    • 3
    • 4
    Email author
  • Chong Lin
    • 3
  • Yuming Chu
    • 5
  • Yongmin Li
    • 5
  1. 1.School of AutomationNanjing University of Science and TechnologyNanjing JiangsuP. R. China
  2. 2.College of Mathematics and Systems ScienceShandong University of Science and TechnologyQingdaoP. R. China
  3. 3.Institute of Complexity ScienceQingdao UniversityQingdaoP. R. China
  4. 4.College of Electrical Engineering and AutomationShandong University of Science and TechnologyQingdaoP. R. China
  5. 5.School of ScienceHuzhou Teachers CollegeHuzhou ZhejiangP. R. China

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