A Data-Driven Automatic Design Method for Electric Machines Based on Reinforcement Learning and Evolutionary Optimization

A Data-Driven Automatic Design Method for Electric Machines Based on Reinforcement Learning and Evolutionary Optimization

The design problems of electric machines are actually treated as a kind of mixed-integer problem, because the machine shapes are defined by some integer variables, such as number of slots, and the other variables, such as the tooth width, which are here called the fundamental and shape variables, re...

書誌詳細
主要な著者:	Takahiro Sato, Masafumi Fujita
フォーマット:	論文
言語:	English
出版事項:	IEEE 2021-01-01
シリーズ:	IEEE Access
主題:	CMA-ES design optimization electric machine evolutionary algorithms reinforcement learning tree search
オンライン･アクセス:	https://ieeexplore.ieee.org/document/9427216/

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