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.

中文翻译：

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开关磁阻电机性能预测中的Moore-Penrose拟逆和人工神经网络建模

目的

本文的目的是提供Moore-Penrose伪逆（PI）建模，并将其与人工神经网络（ANN）建模进行比较，以了解开关磁阻电机（SRM）的性能。

设计/方法/方法

在SRM的设计中，在一定的间隔内凭经验选择了许多参数，因此，要找到最佳的几何形状，必须为SRM定义一个好的模型。拟议的建模使用Moore-Penrose PI求解线性系统和有限元模拟数据。为了证明PI建模的质量，建立了使用ANN的模型，并将这两个模型与有限元模拟确定的值进行比较。

发现

与ANN模型相比，所提出的PI模型具有更好的准确性，泛化能力和较低的计算成本。

创意/价值

只要可以获得实验/计算结果，并且可以为可用数据集提供最佳逼近模型，则所提出的方法可以应用于任何问题。