Scientific Research and Essays

  • Abbreviation: Sci. Res. Essays
  • Language: English
  • ISSN: 1992-2248
  • DOI: 10.5897/SRE
  • Start Year: 2006
  • Published Articles: 2768

Full Length Research Paper

Different types of learning algorithms of artificial neural network (ANN) models for prediction of gross calorific value (GCV) of coals

IÅŸik Yilmaz1*, Nazan Yalcin Erik1 and OÄŸuz Kaynar2
  1Department of Geological Engineering, Faculty of Engineering, Cumhuriyet University, 58140 Sivas, Turkey. 2Department of Management Information System, Cumhuriyet University, 58140 Sivas, Turkey.
Email: [email protected]

  •  Accepted: 15 July 2010
  •  Published: 18 August 2010



Correlations are very significant from earliest days, in some cases, it is essential as it is difficult to measure the amount directly, and in other cases, it is desirable to ascertain the results with other tests through correlations. Soft computing techniques are now being used as alternative statistical tools, and new techniques such as; artificial neural networks, fuzzy inference systems, genetic algorithms, etc. and their hybrid forms have been employed for developing of the predictive models to estimate the needed parameters, in the recent years. Determination of gross calorific value (GCV) of coals is very important to characterize coal and organic shales; it is difficult, expensive, time consuming and is a destructive analysis. In this paper, use of different learning algorithms of artificial neural networks such as MLP, RBF (exact), RBF (k-means) and RBF (SOM) for prediction of GCV was described. As a result of this paper, all models exhibited high performance for predicting GCV. Although the four different algorithms of ANN have almost the same prediction capability, accuracy of MLP has relatively higher than other models. The use of soft computing techniques will provide new approaches and methodologies in prediction of some parameters in the investigations about the fuels.


Key words: ANN, MLP, RBF, soft computing, coal, gross calorific value.