Regressor selection can be viewed as the rst step in the system identication process. The bene ts of nding good regressors before estimating complex models are especially clear for nonlinear systems, where the class of possible models is huge. In this article, a structured way of using the tool Analysis of Variance (ANOVA) AbstractRegressor selection can be viewed as the first step in the system identification process. The benefits of finding good regressors before estimating complex models are especially clear for nonlinear systems, where the class of possible models is huge. In this article, a structured way of using the tool Analysis of Variance (ANOVA) is presented and used for NARX model (nonlinear autoregressive model with exogenous input) identification with many candidate regressors.

Saab Aeronautics has chosen Modelica and Dymola as part of the means for model based system engineering (MBSE). This paper will point out why a considerable effort has been made to migrate models from other simulation tools to Dymola. The paper also shows how the models and tools are used, experiences gained from usage in an industrial context as well as some remaining trouble spots.

Identification of non-linear finite impulse response (N-FIR) models is studied. In particular the selection of model structure, i.e., to find the best regressors, is examined.In this report it is shown that a statistical method, the analysis of variance, is a better alternative than exhaustive search among all possible regressors, in the identification of the structure of non-linear FIR-models. The method is evaluated for different conditions on the input signal to the system. The results will serve as a foundation for the extension of the ideas to non-linear autoregressive processes. Abstract: Identification of non-linear finite impulse response (N-FIR) models is studied. In particular the selection of model structure, i.e., to find the best regressors, is examined. In this paper it is shown that a statistical method, the analysis of variance, is a better alternative than exhaustive search among all possible regressors, in the identification of the structure of non-linear FIR-models. The method is evaluated for different conditions on the input signal to the system. The results will serve as a foundation for the extension of the ideas to non-linear autoregressive processes.

Identification of non-linear finite impulse response (N-FIR) models is studied. In particular the selection of model structure, i.e., to find the best regressors, is examined.In this report it is shown that a statistical method, the analysis of variance, is a better alternative than exhaustive search among all possible regressors, in the identification of the structure of non-linear FIR-models. The method is evaluated for different conditions on the input signal to the system. The results will serve as a foundation for the extension of the ideas to non-linear autoregressive processes. Abstract: Identification of non-linear finite impulse response (N-FIR) models is studied. In particular the selection of model structure, i.e., to find the best regressors, is examined. In this paper it is shown that a statistical method, the analysis of variance, is a better alternative than exhaustive search among all possible regressors, in the identification of the structure of non-linear FIR-models. The method is evaluated for different conditions on the input signal to the system. The results will serve as a foundation for the extension of the ideas to non-linear autoregressive processes.

Fuel tanks in fighter aircraft have an irregular shape which is given by a detailed CAD model. To simulate a fuel system with sufficient amount of detail to solve the design issues, necessary geometrical information need to be given in a compact and computationally fast form. A function approximation using radial basis functions is suggested and compared with some other methods. The complete process from production scale CAD model to system simulation model is considered.