Stress Optimization of High-speed Permanent Magnet Motor Based on Few-shot Transfer Learning Surrogate Model
This paper proposes a transfer learning radial basis function neural network surrogate model to improve the computational efficiency of the sample space of the traditional motor surrogate model.The sample data of this model originate from enough low-fidelity data that can be quickly obtained or prior cumulative and few high-fidelity data.Based on the stress optimization problem of the high-speed permanent magnet motor,the learning ability of the model with few-shot samples,the learning ability to similar topological structures,and the influence of the radial basis function of the model are studied,and this model is proved efficient in the engineering optimization field.Finally,the development trend of transfer learning technology in motor optimization is prospected.
transfer learningsurrogate modelradial basis function neural networkhigh-speed permanent magnet motormotor optimization
国家科技期刊平台
NSTL
万方数据
