Optimising linear regression for modelling the dynamic thermal behaviour of electrical machines using NSGA-II, NSGA-III and MOEA/D. (2023)
Conference Proceeding
BANDA, T.M., ZĂVOIANU, A.-C., PETROVSKI, A., WÖCKINGER, D. and BRAMERDORFER, G. 2024. Optimising linear regression for modelling the dynamic thermal behaviour of electrical machines using NSGA-II, NSGA-III and MOEA/D. In Stratulat, S., Marin, M., Negru, V. and Zaharie, D. (eds.) Proceedings of the 25th International symposium on symbolic and numeric algorithms for scientific computing (SYNASC 2023), 11-14 September 2023, Nancy, France. Los Alamitos: IEEE Computer Society [online], pages 186-193. Available from: https://doi.org/10.1109/SYNASC61333.2023.00032

For engineers to create durable and effective electrical assemblies, modelling and controlling heat transfer in rotating electrical machines (such as motors) is crucial. In this paper, we compare the performance of three multi-objective evolutionary... Read More about Optimising linear regression for modelling the dynamic thermal behaviour of electrical machines using NSGA-II, NSGA-III and MOEA/D..