• DocumentCode
    2951358
  • Title

    Input-Output Stability for Differential Neural Networks

  • Author

    Moran, E.G. ; Labastida, Daishi A Murano

  • Author_Institution
    Dept. de Mec. y Mecatronica, Inst. Tecnol. y de Estudios Super. de Monterrey, Atizapan de Zaragoza, Mexico
  • fYear
    2011
  • fDate
    15-18 Nov. 2011
  • Firstpage
    53
  • Lastpage
    58
  • Abstract
    This paper deals with the problem to obtain input-output stability for a certain class of differential neural networks. Hence, by using a Lyapunov function, the conditions to guarantee finite-gain L-stability, which also ensures global exponential stability (GES), are established. Finally, the simulation of a numerical example illustrates the applicability of this approach.
  • Keywords
    Lyapunov methods; asymptotic stability; neural nets; numerical analysis; Lyapunov function; differential neural networks; finite-gain L-stability; global exponential stability; input-output stability; Asymptotic stability; Equations; Lyapunov methods; Mathematical model; Neural networks; Numerical stability; Stability analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronics, Robotics and Automotive Mechanics Conference (CERMA), 2011 IEEE
  • Conference_Location
    Cuernavaca, Morelos
  • Print_ISBN
    978-1-4577-1879-3
  • Type

    conf

  • DOI
    10.1109/CERMA.2011.16
  • Filename
    6125809
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2951358