Journal of Intelligent and Robotic Systems

, Volume 16, Issue 2, pp 185–207 | Cite as

Selection of input variables for model identification of static nonlinear systems

  • Andreas Bastian
  • Jorge Gasós
Article

Abstract

System identification can be divided into structure and parameter identification. In most system-identification approaches the structure is presumed and only a parameter identification is performed to obtain the coefficients in the functional system. Yet, often there is little knowledge about the system structure. In such cases, the first step has to be the identification of the decisive input variables. In this paper a black-box input variable identification approach using feedforward neural networks is proposed.

Key words

Model identification static nonlinear systems feedforward neural networks structure identification 

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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Andreas Bastian
    • 1
  • Jorge Gasós
    • 1
  1. 1.Laboratory for International Fuzzy Engineering Research (LIFE)Yokohama 231Japan

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