Application of multi-parameter data visualization by means of autoassociative neural networks to evaluate classification possibilities of various coal types
 
 
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AGH University of Science and Technology
CORRESPONDING AUTHOR
Dariusz Jamroz   

AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Applied Computer Science, al. Mickiewicza 30, 30-059 Krakow, Poland
Publication date: 2014-01-06
 
Physicochem. Probl. Miner. Process. 2014;50(2):719–734
 
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ABSTRACT
The significance of data visualization in modern research is growing steadily. In mineral processing scientists have to face many problems with understanding data and finding essential variables from a large amount of data registered for material or process. Hence it is necessary to apply visualization of such data, especially when a set of data is multi-parameter and very complex. This paper puts forward a proposal to introduce the autoassociative neural networks for visualization of data concerning three various types of hard coal. Apart from theoretical discussion of the method, the empirical applications of the method are presented. The results revealed that it is a useful tool for a researcher facing a complicated set of data which allows for its proper classification. The optimal neural network parameters to successfully separate the analyzed three types of coal were found out for the analyzed example.
eISSN:2084-4735
ISSN:1643-1049