SELECTION OF INPUT VARIABLES FOR MODEL IDENTIFICATION OF STATIC NONLINEAR-SYSTEMS

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