A novel classification method for predicting the casting behaviour in the steelmaking practice

Authors:
M. Vannucci;V. Colla
Affiliations:
PERCRO Lab, Scuola Superiore S.Anna, Pontedera, PI, Italy;PERCRO Lab, Scuola Superiore S. Anna, Pontedera, PI, Italy
Venue:
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Year:
2006

Citing 5
Cited 0

Learning internal representations by error propagation

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Universal approximation using radial-basis-function networks

Neural Computation
ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network

Neural Networks
Pattern Recognition by Self-Organizing Neural Networks

Pattern Recognition by Self-Organizing Neural Networks
Neural Networks for Process Control in Steel Manufacturing

ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper presents a novel classification method, the Model-based algorithm, which is based on the principle of classifying the input patterns on the basis of their resemblance to some predefined models.The proposed algorithm has been applied, together with other traditional algorithms, in order to process data coming from a real industrial context. The overall purpose of the work is the prediction of the occurrence of a critical situation during continuous casting in common steelmaking practice and this task is pursued by dividing the data in two classes corresponding to good and bad casting behaviour respectively. The classifiers' performances are heavily affected by the fact that one of the classes to be recognised is poorly represented in the available database.The performance obtained by the different techniques on the experimental data are compared and discussed.