1. Moore-Penrose pseudo-inverse and artificial neural network modeling in performance prediction of switched reluctance machine.
- Author
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Ferreira Mamede, Ana Camila, Camacho, José Roberto, Araújo, Rui Esteves, and Peretta, Igor Santos
- Subjects
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ARTIFICIAL neural networks , *NETWORK performance , *SWITCHED reluctance motors , *RELUCTANCE motors , *LINEAR systems , *PREDICTION models , *ELECTRONIC equipment - Abstract
Purpose: The purpose of this paper is to present the Moore-Penrose pseudoinverse (PI) modeling and compare with artificial neural network (ANN) modeling for switched reluctance machine (SRM) performance. Design/methodology/approach: In a design of an SRM, there are a number of parameters that are chosen empirically inside a certain interval, therefore, to find an optimal geometry it is necessary to define a good model for SRM. The proposed modeling uses the Moore-Penrose PI for the resolution of linear systems and finite element simulation data. To attest to the quality of PI modeling, a model using ANN is established and the two models are compared with the values determined by simulations of finite elements. Findings: The proposed PI model showed better accuracy, generalization capacity and lower computational cost than the ANN model. Originality/value: The proposed approach can be applied to any problem as long as experimental/computational results can be obtained and will deliver the best approximation model to the available data set. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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