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An improved prediction algorithm of seamless tubing corrosion based on an extension neural network
Author(s) -
Wang Tichun,
Tong Changsheng,
Yao Shenghu
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4801
Subject(s) - artificial neural network , corrosion , python (programming language) , computer science , extension (predicate logic) , extensibility , algorithm , artificial intelligence , materials science , metallurgy , programming language
Summary In view of the fact that the corrosiveness of seamless steel tubes under actual conditions was difficult to predict, the main influencing factors of seamless oil tube corrosion under actual conditions were analyzed, and a model for predicting the corrosion of seamless oil tubes was constructed. The algorithm of the extension neural network was introduced. The Python language was used to implement the extensible neural network algorithm, and the influencing factors of steel tube corrosion were trained. The extension neural network was simulated and verified, and finally, the system is implemented by the mobile APP.