Manufacturing maturity grade determination method based on BP-Adaboost algorithm

According to the manufacturing maturity grade determination method based on the BP-Adaboost algorithm, expert evaluation experience is achieved through manufacturing maturity evaluation historical data inheritance, the influence of expert evaluation subjectivity is reduced, and evaluation efficiency...

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Bibliographic Details
Main Authors XUE SHANLIANG, ZHANG HUI, FANG DANFENG, XU MEIJIAO
Format Patent
LanguageChinese
English
Published 31.01.2023
Subjects
CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Summary:According to the manufacturing maturity grade determination method based on the BP-Adaboost algorithm, expert evaluation experience is achieved through manufacturing maturity evaluation historical data inheritance, the influence of expert evaluation subjectivity is reduced, and evaluation efficiency is improved. Comprising the following steps: 1, establishing a manufacturing maturity grade calculation model based on a BP neural network according to manufacturing maturity evaluation historical data; 2, using an Adaboost algorithm to optimize the manufacturing maturity level to calculate a BP neural network model; and 3, training the manufacturing maturity grade calculation model by adopting a general data set similar to the assembly manufacturing maturity evaluation index to obtain a satisfactory manufacturing maturity grade calculation model. According to the method, manufacturing maturity evaluation is carried out based on the BP-Adaboost algorithm, the BP neural network model is optimized by using the Adabo
Bibliography:Application Number: CN202211382066