# Optimization of a Multi-Type PMSM Based on Pyramid Neural Network
## 1 Introduction
Industry and researchers have been designing numerical solvers for decades to enable the rapid analysis of electromagnetic fields in motors. However, in the iterative process of optimization, the accurate finite element analysis of the electromagnetic field inside the motor usually requires a large amount of computational time. Therefore, numerous researchers began to try to use various agents, hoping to calculate the objective function in the motor optimization problem using the fast approximation method, so as to considerably reduce the computation time of finite element analysis [16]. For example, the response surface model (RSM) has been applied to the optimization of electric motor design [17]. These methods are usually proposed specifically to a problem. The description of the systems generally contains only a few parameters. Thus, these surrogate models lack the ability to be widely applicable to different types of motor structure design [18]. Recently, many meta-modeling methods based on big data [19] and machine learning have attracted wide attention. Neural networks (NNs) based on fuzzy logic have been used to model the influence of clearance on product quality and the blanking force in blanking process [20].Artificial neural networks (ANNs) have been used to model the nonlinear relationship between the power harvested with a piezoelectric pendulum and its resistive load, magnetic excitation frequency, etc. [21]. A multilayer feed-forward artificial neural network algorithm was proposed and applied to estimate the output power and efficiency of an axial flux permanent magnet synchronous generator (AFPMSG) [22]. Among these meta-modeling methods, the deep learning (DL) algorithm is more related to the current problem of solving [PERSON]'s equation [23]. It has the advantages of scalability of high-dimensional data, the ability to deal with massive data, opening up a new field for pattern recognition and curve fitting of complex problems such as dense regression, easy parallelization and automatic feature extraction. Therefore, the use of neural networks that are appropriate for electromagnetic field simulation problems may accelerate seeking the solution of them. The DL model has particularly strong approximation ability, given a source of dielectric constant and the distribution information of correct predictions. [PERSON] proposed to use the DL model to predict the solution of the Maxwell equation [24], the main electromagnetic calculation equation of electromagnetic equipment, and conducted a lot of research on this topic. The work took a coil, a transformer and a permanent magnet synchronous motor as the research object, and used the data obtained from the traditional finite element calculation. A deep convolutional neural network was used for training based on supervised learning to learn the law of magnetic field distribution [25]. The use of deep networks suitable for magnetic field analysis tasks may accelerate the solution of motor optimization problems. In a deep neural network (DNN) [26], the information extracted from different layers is insufficient and the feature map of different layers is used for prediction. The performance of the network may be affected if the features of some layers in the basic network are not sufficiently obtained. The multi-branch feature pyramid network refers to the feature map of different layers obtained through the bottom-up basic network and top-down transverse connection, and the feature map of different layers can then be predicted. It has been used in various fields such as target detection, image segmentation, salience target detection and image detection. A multi-branch network [27] has one branch referring to a basic network structure, and other branches with other structures. The basic network obtains rough features, and, after adding other branches, obtains richer details or spatial information. The purpose of adding branches is to obtain richer and more detailed features on the basis of rough features.