Motor Parameter Design Using Neural Network and Particle Swarm Optimization

Abstract

In recent years the industrial requirements have been growing consequently the demand of motors in industry automation has increased therefore the production of motors needs to speed up However motor design usually spend the most time waiting the result of simu-lation If the result isn’t expected it will cost several days to modify and delay the new product release Under these demands this thesis focuses on the design of neural network includ-ing network structure and training data set Replacing finite element analysis software with neural network due to its advantages of calculating time In addition this thesis also combine PSO algorithm and neural network to opti-mize the parameters and performance through the target performance selected by the user and set the weight of the performance find the motor parameters that satisfy us-er’s demand Finally use the program to increase the efficiency and power density of target motor and verify it with the analysis of finite element software Not only the perfor-mance that predict by neural network is comparable to finite element software but al-so reduces the redesigning time by using optimization algorithm

Date of Award	2020
Original language	English
Supervisor	Min-Fu Hsieh (Supervisor)