Memory-based State Estimation of T–S Fuzzy Markov Jump Delayed Neural Networks with Reaction–Diffusion Terms

  • Xiaona SongEmail author
  • Jingtao Man
  • Zhumu Fu
  • Mi Wang
  • Junwei Lu


This paper investigates the problem of state estimation for Takagi–Sugeno (T–S) fuzzy Markov jump delayed neural networks with reaction–diffusion terms. A memory-based control scheme that contains a constant signal transmission delay is adopted, which is the first attempt to handle the issue of state estimation for fuzzy neural networks. Firstly, several conditions that guarantee the stability of the considered system are derived. Then, the fuzzy memory-based controller design scheme is proposed. Finally, three numerical examples are given to demonstrate the validity of the proposed method.


Delayed neural networks Markov jump Memory-based control Reaction–diffusion terms T–S fuzzy model 



This work was supported by the National Natural Science Foundation of China under Grant U1604146, and by the Foundation for the University Technological Innovative Talents of Henan Province under Grant 18HASTIT019.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information EngineeringHenan University of Science and TechnologyLuoyangChina
  2. 2.School of Electrical and Automation EngineeringNanjing Normal UniversityNanjingChina

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