Treffer: Prediction of sinter burn-through point based on support vector machines
Title:
Prediction of sinter burn-through point based on support vector machines
Authors:
Source:
Intelligent control and automation (International conference on Intelligent computing, ICIC 2006, Kunming, China, August 16-19, 2006)0ICIC 2006. :722-730
Publisher Information:
Berlin: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 11 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Documentation, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Physique, Physics, Domaines interdisciplinaires: science des materiaux; rheologie, Cross-disciplinary physics: materials science; rheology, Science des matériaux, Materials science, Autres méthodes de préparation et de traitement des matériaux, Materials synthesis; materials processing, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Analyse statistique, Statistical analysis, Análisis estadístico, Apprentissage probabilités, Probability learning, Aprendizaje probabilidades, Complexité temps, Time complexity, Complejidad tiempo, Frittage, Sintering, Sinterización, Machine exemple support, Vector support machine, Máquina ejemplo soporte, Modélisation, Modeling, Modelización, Réseau neuronal, Neural network, Red neuronal, Structure sandwich, Sandwich structure, Estructura sandwich, Technologie poudre, Powder technology, Temps retard, Delay time, Tiempo retardo, Toron, Strand, Bocel
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
School of Mechatronical Engineering and Automation, Shanghai University, Shanghai 200072, China
Automation Department of Laiwu Steel Group, Laiwu 271104, China
Shanghai Fire Research Institute of Ministry of Public Security, Shanghai 200032, China
Automation Department of Laiwu Steel Group, Laiwu 271104, China
Shanghai Fire Research Institute of Ministry of Public Security, Shanghai 200032, China
ISSN:
0170-8643
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Physics and materials science
Physics and materials science
Accession Number:
edscal.18393555
Database:
PASCAL Archive
Weitere Informationen
In order to overcome the long time delays and dynamic complexity in industrial sintering process, a modeling method of prediction of bum-through point (BTP) was proposed based on support vector machines (SVMs). The results indicate SVMs outperform the three-layer Backpropagation (BP) neural network in predicting bum-through point with better generalization performance, and are satisfactory. The model can be used as plant model for the bum-through point control of on-strand sinter machines.