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Please use this identifier to cite or link to this item: http://hdl.handle.net/10651/22829

Title: Visual data mining and monitoring in steel processes
Author(s): Cuadrado Vega, Abel Alberto
Díaz Blanco, Ignacio
Díez González, Alberto Benjamín
Obeso Carrera, Faustino Emilio
González, Juan Antonio
Issue date: 2002
Publisher: IEEE
Format extent: p. 493-500
Abstract: Steel processes are often of a complex nature and difficult to model. All information that we have at hand usually consists of more or less precise models of different parts of the process, some rules obtained on the basis of experience, and typically a great amount of high-dimensional data coming from numerous sensors and variables of process computers which convey a lot of information about the process state. We suggest in this paper the use of a continuous version of the self-organizing map (SOM) to project a high dimensional vector of process data on a 2D visualization space in which different process conditions are represented by different regions. Later, all sorts of information resulting from the fusion of knowledge obtained from data, mathematical models and fuzzy rules can be described in a graphical way in this visualization space
URI: http://hdl.handle.net/10651/22829
ISSN: 0197-2618
Appears in Collections:Ponencias, Discursos y Conferencias
Ingeniería Eléctrica, Electrónica, de Computadores y Sistemas

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