## 3 Machine Learning for the Design Optimization of Electromagnetic Devices
### Machine Learning for Performance Prediction of Electromagnetic Devices
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Model** & **Application** &
\begin{tabular}{c} **Estimation Objectives or** \\ **Optimization Methods** \\ \end{tabular} \\ \hline
[129] & \
\begin{tabular}{c} Convolutional neural \\ network (CNN) \\ \end{tabular} &
\begin{tabular}{c} Electromagnetic devices including \\ transformer and permanent magnet \\ (PM) motor \\ \end{tabular} & Magnetic field estimation \\
[130; 131] & \
\begin{tabular}{c} CNN, recurrent neural \\ network (RNN) \\ \end{tabular} &
\begin{tabular}{c} permanent magnet synchronous \\ motor (PMSMs) \\ \end{tabular} & Temperature Estimation \\
[132] & \begin{tabular}{c} ANN, CNN, RNN \\ \end{tabular} & \
\begin{tabular}{c} Interior PM motors \\ \end{tabular} &
\begin{tabular}{c} Efficiency map and \\ flux-linkage prediction \\ Torque prediction \\ Torque prediction \\ \end{tabular} \\
[133; 134] & \begin{tabular}{c} SVM \\ SVM \\ \end{tabular} & \
\begin{tabular}{c} Switched reluctance motor \\ PMSMs \\ \end{tabular} &
\begin{tabular}{c} Multi-objective optimization \\ Random forest algorithms \\ \end{tabular} \\
[137] & \begin{tabular}{c} Random forest (RF) \\ \end{tabular} & \
\begin{tabular}{c} Induction machine \\ \end{tabular} &
\begin{tabular}{c} Multi-objective optimization, grey \\ wolf optimization algorithm \\ \end{tabular} \\
[138] & \begin{tabular}{c} ELM \\ \end{tabular} & \
\begin{tabular}{c} PM synchronous linear motors \\ \end{tabular} &
\begin{tabular}{c} Differential evolution algorithm \\ Hybrid metaheuristic algorithm \\ \end{tabular} \\
[139] & \begin{tabular}{c} K-nearest neighbor (KNN) \\ \end{tabular} & \
\begin{tabular}{c} PM synchronous linear motors \\ PMSMs \\ \end{tabular} &
\begin{tabular}{c} Differential evolution algorithm \\ Hybrid metaheuristic algorithm \\ \end{tabular} \\
[140] & \begin{tabular}{c} R-DNN (deep \\ neural network) \\ \end{tabular} & \
\begin{tabular}{c} Double secondary linear motor \\ \end{tabular} &
\begin{tabular}{c} Cuckoo search algorithm \\ \end{tabular} \\
[141] & \begin{tabular}{c} CNN \\ \end{tabular} & \
\begin{tabular}{c} Synchronous reluctance motor \\ \end{tabular} &
\begin{tabular}{c} Binary particle swarm \\ optimization algorithm \\ Topology optimization, \\ multi-objective optimization, \\ genetic algorithm \\ Structure optimization \\ Multi-objective and robust \\ design optimization \\ \end{tabular} \\ \hline
[143; 144; 145] & \begin{tabular}{c} CNN \\ \end{tabular} & \
\begin{tabular}{c} Interior PM motors \\ \end{tabular} &
\begin{tabular}{c} Multi-objective optimization, \\ genetic algorithm \\ Structure optimization \\ Multi-objective and robust \\ design optimization \\ \end{tabular} \\ \hline
[146] & \begin{tabular}{c} ANN \\ \end{tabular} & \
\begin{tabular}{c} High-frequency transformer \\ \end{tabular} &
\begin{tabular}{c} **Estimation Objectives or** \\ **Optimization Methods** \\ \end{tabular} \\
[147; 148; 149; 150] & \begin{tabular}{c} ANN, SVM, DNN \\ \end{tabular} & \
\begin{tabular}{c} Antennas \\ \end{tabular} &
\begin{tabular}{c} Binary particle swarm \\ optimization algorithm \\ Topology optimization, \\ multi-objective optimization, \\ genetic algorithm \\ Structure optimization \\ Multi-objective and robust \\ design optimization \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Selective bibliography focusing on electromagnetic device design optimization based on different machine learning methods. Figure 8: (**a**) Flux linkage prediction based on MCC-LSSVR model with respect to phase current and position angle, (**b**) torque prediction based on MCC-LSSVR model with respect to phase current and position angle. Figure 7: Machine topology of the \(16/10\) SRM with segmented rotors and excited and auxiliary stators.