Kocaleva, Mirjana and Zlatev, Zoran and Hinov, Nikolay (2022) The Voltage Prediction of a Buck Converter Using Machine Learning Approaches. In: 10-th International Scientific Conference Computer Science, 30 May - 2 June 2022, Sofia, Bulgaria.
Full text not available from this repository.Abstract
Machine learning is a branch of artificial intelligence and a method of data analysis that automates analytical model building. It is based on the idea that systems can learn alone from data, identify patterns, and make decisions without or with minimal human help. The paper reviews the machine learning as a process for teaching computers to learn from experience or directly from data, being based on a predefined equation as a model. We used four types of decision tree as machine learning methods for data set classification, such as PERTree, M5P, RandomTree and RandomForest. First, we give the equations for buck converter as a model, then we teach the computer to make predictions by his own. The Buck DC-DC converter is known as a “step down” converter and so the output voltage will always be less than or equal to the input voltage. Second, the way we gain the database and WEKA software are described. WEKA operate with .arff file format, so we first convert our database in the required format. Then we present and discuss the results obtained using different types of classification.
| Item Type: | Conference or Workshop Item (Speech) |
|---|---|
| Subjects: | Natural sciences > Computer and information sciences |
| Divisions: | Faculty of Computer Science |
| Depositing User: | Mirjana Kocaleva Vitanova |
| Date Deposited: | 15 Dec 2022 12:30 |
| Last Modified: | 15 Dec 2022 12:30 |
| URI: | https://eprints.ugd.edu.mk/id/eprint/29782 |
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