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Effect of Training Algorithms on the Performance of ANN for Pattern Recognition of Bivariate Process

by Olatunde A. Adeoti, Peter A. Osanaiye
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 20
Year of Publication: 2013
Authors: Olatunde A. Adeoti, Peter A. Osanaiye
10.5120/12085-8031

Olatunde A. Adeoti, Peter A. Osanaiye . Effect of Training Algorithms on the Performance of ANN for Pattern Recognition of Bivariate Process. International Journal of Computer Applications. 69, 20 ( May 2013), 8-12. DOI=10.5120/12085-8031

@article{ 10.5120/12085-8031,
author = { Olatunde A. Adeoti, Peter A. Osanaiye },
title = { Effect of Training Algorithms on the Performance of ANN for Pattern Recognition of Bivariate Process },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 20 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 8-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number20/12085-8031/ },
doi = { 10.5120/12085-8031 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:30:45.938072+05:30
%A Olatunde A. Adeoti
%A Peter A. Osanaiye
%T Effect of Training Algorithms on the Performance of ANN for Pattern Recognition of Bivariate Process
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 20
%P 8-12
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Artificial Neural Network (ANN) which is designed to mimic the human brain have been used in the literature for identifying variable(s) that is(are) responsible for out-of-control signal and the training algorithms have played a significant role in the identification of the aberrant variable(s). In this paper the effect of three algorithms in the training of ANN for pattern recognition of bivariate process is studied. Situations in which the algorithms performed satisfactorily with respect to recognition accuracy (in percentages), epochs and MSE were identified. The result of study shows that the Levenberg-Marquardt (trainlm) is the best algorithm for pattern recognition of bivariate manufacturing process in terms of recognition accuracy and the resilient backpropagation (trainrp) is best in terms of speed and mean square error performance.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial neural network training algorithms feedforward multilayer perceptron recognition accuracy

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