Transformation and weighting in regression
Transformation and weighting in regression
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Smart Archive: a Component-based Data Mining Application Framework
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Building cost functions minimizing to some summary statistics
IEEE Transactions on Neural Networks
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The purpose of this study was to develop a product design model for estimating the impact toughness of low-alloy steel plates. The rejection probability in a Charpy-V test (CVT) is predicted with process variables and chemical composition. The proposed method is suitable for the whole production line of a steel plate mill, including all grades of steel in production. The quantile regression model was compared to the joint model of mean and dispersion and the constant variance model. The quantile regression model proved out to be the most effective method for modelling a highly complicated property at this extent. Next, the developed model will be implemented into a graphical simulation tool that is in daily use in the product planning department and already contains some other mechanical property models. The model will guide designers in predicting the related risk of rejection and in producing desired properties in the product at lower cost.