A Prediction Model for Drilling Speed of Drilling Machin-ery Based on GA-BPANN
During the drilling process,optimizing drilling tech-niques can reduce drilling costs and construction accidents,and drilling speed prediction is the foundation of optimizing drilling.In order to improve the accuracy of the mechanical drilling speed(ROP)prediction model,this paper developed a genetic algorithm optimized BP artificial neural network(GA-BPANN)ROP predic-tion model.Firstly,the maximum information coefficient(MIC)method is used for feature selection to reduce model redundancy,and the data is standardized.Secondly,using genetic algorithm(GA)to optimize the initial weights and biases of BPANN,a new ROP prediction model is established.Finally,compare and analyze the new model with BPANN and Support Vector Regression(SVR)models.The research results indicate that the GA-BPANN model has high prediction accuracy and provides scientific basis for improving ROP during the drilling process.
NETL
NSTL
万方数据
维普
