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

• Ana Camila Ferreira Mamede ^[1] ; José Roberto Camacho ^[1] ; Rui Esteves Araújo ^[2] ; Igor Santos Peretta ^[1]
  1. [1] Universidade Federal de Uberlândia

     Universidade Federal de Uberlândia

     Brasil

     
  2. [2] Universidade Do Porto

     Universidade Do Porto

     Santo Ildefonso, Portugal

     
• Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 39, Nº 6, 2020, págs. 1411-1430
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • 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.