• DocumentCode
    428734
  • Title

    A neural network model and its application

  • Author

    Meiqin, Liu ; Senlin, Zhang ; Gangfeng, Yan ; Shouguang, Wang

  • Author_Institution
    Coll. of Electr. Eng., Zhejiang Univ., Hangzhou, China
  • Volume
    6
  • fYear
    2004
  • fDate
    10-13 Oct. 2004
  • Firstpage
    5864
  • Abstract
    A novel neural network model called standard neural network model (SNNM) was advanced, which was the interconnection of a linear dynamic system and a bounded static nonlinear operator. The SNNM could be represented by linear differential inclusion (LDI), which allowed us to take advantage of the linear matrix inequality (LMI) approach in the stability analysis or other performance analysis of the SNNM. By combining a number of different Lyapunov functions with S-procedure, some useful stability theorems for continuous time SNNM (CSNNM) and discrete time SNNM (DSNNM) were derived, whose conditions were formulated as LMIs. Some examples for the application of the SNNMs were presented, such as analyzing the stability of recurrent neural network (RNN), analyzing or synthesizing the neural network control system etc.
  • Keywords
    Lyapunov methods; continuous time systems; discrete time systems; linear differential equations; linear matrix inequalities; linear systems; neurocontrollers; nonlinear control systems; recurrent neural nets; LMI; Lyapunov functions; bounded static nonlinear operator; linear differential inclusion; linear dynamic system interconnection; linear matrix inequality; neural network control system; recurrent neural network; stability theorems; standard neural network model; Asymptotic stability; Cellular neural networks; Control system synthesis; Control systems; Hopfield neural networks; Mathematical model; Neural networks; Performance analysis; Recurrent neural networks; Stability analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics, 2004 IEEE International Conference on
  • ISSN
    1062-922X
  • Print_ISBN
    0-7803-8566-7
  • Type

    conf

  • DOI
    10.1109/ICSMC.2004.1401131
  • Filename
    1401131