Open AccessTransformer optimization system design based on deep learning and evolutionary algorithm
Author(s) -
Hongjia He,
Peipei Li,
Hang Zhou,
Yiyi Jiang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1827/1/012084
Subject(s) - inertia , transformer , particle swarm optimization , artificial neural network , computer science , backpropagation , voltage , algorithm , value (mathematics) , artificial intelligence , machine learning , engineering , electrical engineering , physics , classical mechanics
A single shallow learning algorithm cannot fit the characteristics of high-voltage reactors well. Aiming at the above problems, this paper uses the error back propagation neural network and the particle swarm algorithm optimized by adaptive inertia weight to optimize the combined prediction model B for data training, verification and testing are carried out to achieve the purpose of effectively reducing the manufacturing cost of high-voltage reactors. Through experimental verification, the maximum error between the predicted value and the true value is 2.9%, and the minimum error is 0.05%. This provides certain technical support and inspiration for future devices such as optimizing high-voltage reactors.