Moore-Penrose pseudo-inverse and artificial neural network modeling in performance prediction of switched reluctance machine

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Mamede, ACF (Author) Other; Camacho, JR (Author) Other; Rui Esteves Araújo (Author) FEUP; Peretta, IS (Author) Other

Journal

Title: COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

Vol. 39

Pages: 1411-1430

ISSN: 0332-1649

Publisher: Emerald

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-00T-2Z2

DOI: 10.1108/compel-11-2019-0449

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 20