Szczegóły publikacji
Opis bibliograficzny
Filtering of industrial data using the artificial neural networks — Filtrowanie przemysłowych danych przy wykorzystaniu sztucznych sieci neuronowych / Andrzej STANISŁAWCZYK, Jolanta TALAR, Piotr JAROSZ, Jan KUSIAK // Computer Methods in Materials Science : quarterly / Akademia Górniczo-Hutnicza ; ISSN 1641-8581. — Tytuł poprz.: Informatyka w Technologii Materiałów. — 2007 — vol. 7 no. 2, s. 311–316. — Bibliogr. s. 315, Abstr., Streszcz. — 14th KomPlasTech conference : Zakopane, Poland, January 14–17, 2007
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 32309 |
|---|---|
| Data dodania do BaDAP | 2007-03-16 |
| Tekst źródłowy | URL |
| Rok publikacji | 2007 |
| Typ publikacji | referat w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Computer Methods in Materials Science |
Abstract
The data sets, which describe the parameters of any real industrial process, are usually noisy and often not complete. Moreover, the analysis of the data sets is complicated because of the measurement noise of different kinds. The filtering process of the data with the imposed noise is a complex problem and it is difficult to find appropriate general filtering method, which gives the reliable results. Sometimes, the filtering procedure eliminates important information, and sometimes leaves the unnecessary noise. This situation causes many problems with the gathering of the data, which can be useful in modelling of the considered industrial process. The main objective of the work is elaboration of the filtering procedures of the data sets obtained from the copper flash smelting process. The filtered data will serve to work out the Artificial Neural Network based control system of the copper flash smelting process. The existing models of that process are very simplified or based on the FEM models [1-4]. These models are useless from the point of view of the control system, because of its low accuracy and a long computation time. The idea of the control system of the copper flash smelting process is based on the artificial neural networks model [5,6]. The registered in the industrial conditions data are not suitable for the further analysis and modelling of the process. Therefore, the elaboration of the model must be preceded with the filtering of the data. Different techniques were applied to filtering of the noisy industrial data: Fourier Transform Method and techniques based on the Artificial Neural Networks. The filtered data sets were used to creation of the ANN model of the copper flash smelting process. The paper presents the results of filtering of the industrial data and the results of prediction of the chosen parameters of the copper flash smelting process using ANN model elaborated on the base of filtered data.
