@inproceedings{9c266a2cc3a240c1b0ce86e03557d679,
title = "A Parameter Identification Method Based on Data and Physical Information",
abstract = "Data-driven parameter identification methods are prone to deviating from physical meaning and falling into local optimal solutions. To address this issue, this paper proposes a novel parameter identification approach that integrates both data and physical information. The contributions of this work include achieving accurate parameter identification by integrating physical models with machine learning, requiring less data, and eliminating the need for external signal injection. The method is applied to power electronic converters, and simulation results demonstrate its effectiveness and superiority over traditional methods in terms of accuracy and robustness.",
keywords = "Parameter identification, physics-informed machine learning, power electronic converters",
author = "Cuiyu Liu and Ziwen Xiao and Gang Xiang and Zhiming Yang and Yang Yu",
note = "Publisher Copyright: {\textcopyright} 2026 The Authors.; 2nd Annual International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2025 ; Conference date: 12-12-2025 Through 14-12-2025",
year = "2026",
month = mar,
day = "13",
doi = "10.3233/ATDE260192",
language = "英语",
series = "Advances in Transdisciplinary Engineering",
publisher = "IOS Press BV",
pages = "71--78",
editor = "Jesus, \{Isabel S.\} and Ke Wang",
booktitle = "Intelligent Manufacturing and Cloud Computing - Proceedings of the 2nd International Conference, ICIMCC 2025",
address = "荷兰",
}