• DocumentCode
    1290314
  • Title

    Observer Design for Switched Recurrent Neural Networks: An Average Dwell Time Approach

  • Author

    Lian, Jie ; Feng, Zhi ; Shi, Peng

  • Author_Institution
    Fac. of Electron. Inf. & Electr. Eng., Dalian Univ. of Technol., Dalian, China
  • Volume
    22
  • Issue
    10
  • fYear
    2011
  • Firstpage
    1547
  • Lastpage
    1556
  • Abstract
    This paper is concerned with the problem of observer design for switched recurrent neural networks with time-varying delay. The attention is focused on designing the full-order observers that guarantee the global exponential stability of the error dynamic system. Based on the average dwell time approach and the free-weighting matrix technique, delay-dependent sufficient conditions are developed for the solvability of such problem and formulated as linear matrix inequalities. The error-state decay estimate is also given. Then, the stability analysis problem for the switched recurrent neural networks can be covered as a special case of our results. Finally, four illustrative examples are provided to demonstrate the effectiveness and the superiority of the proposed methods.
  • Keywords
    asymptotic stability; computability; delays; linear matrix inequalities; observers; recurrent neural nets; time-varying systems; average dwell time approach; delay-dependent sufficient condition; error dynamic system; error-state decay estimate; free-weighting matrix technique; global exponential stability; linear matrix inequalities; observer design; solvability; stability analysis; switched recurrent neural network; time-varying delay; Biological neural networks; Delay; Neurons; Observers; Recurrent neural networks; Stability analysis; Switches; Average dwell time method; exponential stability; observer design; switched neural networks; time-varying delay; Algorithms; Artificial Intelligence; Humans; Linear Models; Neural Networks (Computer); Observation; Software Design; Software Validation; Time Factors;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2011.2162111
  • Filename
    5975222