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pro vyhledávání: '"Omid Khalaf Beigi"'
Publikováno v:
IEEE Access, Vol 12, Pp 129484-129497 (2024)
Integrating the feature selection process into machine learning-based transient analysis (MLTA) is essential for ensuring precise and timely transient stability prediction (TSP). Therefore, it’s important to prioritize the development of a hybrid f
Externí odkaz:
https://doaj.org/article/488f92a35fd64e60932c3ad0f9a6dca8
Publikováno v:
Journal of Artificial Intelligence and Data Mining, Vol 11, Iss 4, Pp 627-638 (2023)
A speedy and accurate transient stability assessment (TSA) is gained by employing efficient machine learning- and statistics-based (MLST) algorithms on transient nonlinear time series space. In the MLST’s world, the feature selection process by for
Externí odkaz:
https://doaj.org/article/c2bc6c6460bf48daacfa4ebe9dc822ef