International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 55 - Issue 10 |
Published: October 2012 |
Authors: Olatunde A. Adeoti, Rotimi F. Afolabi |
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Olatunde A. Adeoti, Rotimi F. Afolabi . Pattern Recognition of Process Mean Shift using Combined ANN Recognizer. International Journal of Computer Applications. 55, 10 (October 2012), 15-19. DOI=10.5120/8789-2774
@article{ 10.5120/8789-2774, author = { Olatunde A. Adeoti,Rotimi F. Afolabi }, title = { Pattern Recognition of Process Mean Shift using Combined ANN Recognizer }, journal = { International Journal of Computer Applications }, year = { 2012 }, volume = { 55 }, number = { 10 }, pages = { 15-19 }, doi = { 10.5120/8789-2774 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2012 %A Olatunde A. Adeoti %A Rotimi F. Afolabi %T Pattern Recognition of Process Mean Shift using Combined ANN Recognizer%T %J International Journal of Computer Applications %V 55 %N 10 %P 15-19 %R 10.5120/8789-2774 %I Foundation of Computer Science (FCS), NY, USA
Artificial Neural Network (ANN) based model has been proposed for diagnosis of process mean shift. These are mainly generalized-based where only a single classifier was applied in the diagnosis of abnormal pattern. In this paper, we analyze the performance of a combined recognizer consisting of small-sized artificial neural networks on varying number of nodes in the hidden layer trained with Levenberg Marquardt and Quasi-Newton Algorithm. The results of our study illustrate the effectiveness of the combined recognizer and showed that combined recognizer performed better when number of hidden nodes is small, say, less than 15 in terms of recognition accuracies and mean square error as compared to the single recognizer.