Academic Journals Database
Disseminating quality controlled scientific knowledge

Generalized profile function model based on neural networks

Author(s): Radonja Pero | Stanković Srđan

Journal: Serbian Journal of Electrical Engineering
ISSN 1451-4869

Volume: 6;
Issue: 2;
Start page: 285;
Date: 2009;
VIEW PDF   PDF DOWNLOAD PDF   Download PDF Original page

Keywords: generalized profile function model | individual stem profile model | neural networks | histogram | scatter plot

Generalized profile function model (GPFM) provides approximations of the individual models (individual stem profile models) of the objects using only two basic measurements. In this paper it is shown that this GPFM can be successfully derived by using artificial computational intelligence, that is, neural networks. GPFM is obtained as a mean value of all the available normalized individual models. Generation of GPFM is performed by using the basic dataset, and verification is done by using the validation data set. Statistical properties of the original, measured data and estimated data based on the generalized model are presented and compared. Testing of the obtained GPFM is performed also by the regression analysis. The obtained correlation coefficients between the real data and the estimated data are very high, 0.9946 for the basic data set, and 0.9933 for the validation dataset. .

Tango Jona
Tangokurs Rapperswil-Jona

     Affiliate Program