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

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]