Open AccessResearch On Neural Network Quality Prediction Model Based On Genetic Algorithm
Author(s) -
Xia Li,
Yiru Dai,
Jin Cheng
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/267/4/042026
Subject(s) - artificial neural network , genetic algorithm , computer science , production (economics) , quality (philosophy) , production line , data mining , product (mathematics) , process (computing) , industrial production , algorithm , artificial intelligence , machine learning , engineering , mathematics , philosophy , epistemology , mechanical engineering , geometry , keynesian economics , economics , macroeconomics , operating system
In industrial production process, it is difficult to predict product quality in advance. Traditional prediction methods are mostly based on complex mechanism models, and the prediction accuracy is not high. This paper uses the historical data of industrial production to forecast, constructs the quality prediction model of neural network, and uses genetic algorithm to optimize the network parameters, so as to avoid the neural network falling into local optimum. This paper takes the data of hot rolling production line as an example and adopts the method to predict the quality index of steel plate. The simulation result shows that the quality prediction model of neural network based on genetic algorithm has better prediction ability. This method has important theoretical value and practical significance for production workers to improve product quality.