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International Journal of Fuzzy Systems

, Volume 21, Issue 2, pp 532–544 | Cite as

Event-Triggered State Estimation for T–S Fuzzy Neural Networks with Stochastic Cyber-Attacks

  • Jinliang LiuEmail author
  • Tingting Yin
  • Xiangpeng Xie
  • Engang Tian
  • Shumin Fei
Article
  • 198 Downloads

Abstract

This paper is mainly concerned with event-triggered state estimation for Takagi–Sugeno (T–S) fuzzy neural networks subjected to stochastic cyber-attacks. An event-triggered scheme is utilized to decide whether the sampled data should be delivered or not. By taking the influence of the cyber-attacks into consideration, a T–S fuzzy model for the state estimation of neural networks is established with the event-triggered scheme. Through the utilization of Lyapunov stability theory and linear matrix inequality (LMI) techniques, the sufficient conditions are derived which can ensure the stability of estimator error systems. In addition, the gains of the estimator are acquired in the form of LMIs. Finally, a simulated example is presented to illustrate the effectiveness of the proposed method.

Keywords

Event-triggered scheme T–S fuzzy neural networks Stochastic cyber-attacks State estimation 

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Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Jinliang Liu
    • 1
    • 2
    Email author
  • Tingting Yin
    • 1
  • Xiangpeng Xie
    • 3
  • Engang Tian
    • 4
  • Shumin Fei
    • 5
  1. 1.College of Information EngineeringNanjing University of Finance and EconomicsNanjingChina
  2. 2.Key Laboratory of Grain Information Processing and ControlHenan University of Technology, Ministry of EducationHenanChina
  3. 3.Institute of Advanced TechnologyNanjing University of Posts and TelecommunicationsNanjingChina
  4. 4.School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina
  5. 5.School of AutomationSoutheast UniversityNanjingChina

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