A Comparison Between Levenberg-Marquardt (LM) Intelligent System And Bayesian Regularization (BR) Intelligent System For Flow Regime Classification
The purpose of this project is to study the performance, leaning time and, output of Levenberg-Marquardt (LM) intelligent system and Bayesian Regularization (BR) intelligent system through a classification problem. These studies will help in choosing the right training algorithm for classification...
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| Format: | Monograph |
| Language: | English |
| Published: |
Universiti Sains Malaysia
2006
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| Online Access: | http://eprints.usm.my/58548/1/A%20Comparison%20Between%20Levenberg-Marquardt%20%28LM%29%20Intelligent%20System%20And%20Bayesian%20Regularization%20%28BR%29%20Intelligent%20System%20For%20Flow%20Regime%20Classification_Mohamad%20Iqbal%20Sa%27ad.pdf http://eprints.usm.my/58548/ |
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| Institution: | Universiti Sains Malaysia |
| Language: | English |
| Summary: | The purpose of this project is to study the performance, leaning time and, output of Levenberg-Marquardt (LM) intelligent system and Bayesian Regularization (BR)
intelligent system through a classification problem. These studies will help in choosing the right training algorithm for classification problem involved. These intelligent systems
have to classify flow regimes in a closed line with the data are provided by Electrical Capacitance Tomography (ECT). ECT measured the different capacitance value of fluid
and produced the data for the classification problem. Multilayed Perceptron (MLP), a type of artificial neural network (ANN) which is widely used in a classification problem
is developed using MATLAB 7®. The comparison made showed that LM learning algortihm is a faster training algorithm compared to BR training algorithm meanwhile BR
learning algorithm capable of building a superior intelligent system in term of the overall
system performance. |
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