Resumen
Mathematical model selection method on the basis of a balance between the complexity and the experimental data fitting accuracy is proposed. The method is based on: 1) a set of (parametric) families of models suitable for satisfactory reproduction of measurements; 2) model complexity notion formalization (for the selected family of models); 3) a cross-validation procedure for estimating the error of data modeling; 4) search for the optimal trade-off between the complexity of the model and proximity to measurements based on minimizing the cross-validation error of data modeling. The procedure is explained by the demonstrative case study. General mathematical statement of model evaluation problem is presented. The issues of software implementation in a distributed computing environment are discussed.