Publication: A comparison between levenberg-marquardt (lm) intelligent system and bayesian regularization (br) intelligent system for flow regime classification
| datacite.subject.fos | oecd::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering | |
| dc.contributor.author | Sa'ad, Mohamad Iqbal | |
| dc.date.accessioned | 2024-05-27T09:13:12Z | |
| dc.date.available | 2024-05-27T09:13:12Z | |
| dc.date.issued | 2006-05-01 | |
| dc.description.abstract | 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. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/19347 | |
| dc.language.iso | en | |
| dc.title | A comparison between levenberg-marquardt (lm) intelligent system and bayesian regularization (br) intelligent system for flow regime classification | |
| dc.type | Resource Types::text::report | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |