Research on Improved BP Neural Network Blast Vibration Frequency Prediction Model

Autor: Ren Yujie, Chenglong Yu, Ma Ruixin, Wenjing Wang, Tian Xingqiang, Xunxian Shi, Shengxiang Ma
Rok vydání: 2020
Předmět:
Zdroj: 2020 IEEE International Conference on Information Technology,Big Data and Artificial Intelligence (ICIBA).
DOI: 10.1109/iciba50161.2020.9276997
Popis: In order to improve the accuracy of the blasting vibration frequency prediction model, a factor analysis method is combined with the BP neural network method to propose an improved BP neural network blasting vibration frequency prediction model. The actual field data of the project is selected to test the accuracy of the improved BP neural network network blasting vibration frequency prediction model. The final result: the relative average error between the predicted value and the actual value of the 20 groups of training samples is 4.89%. The errors are 6.75%, 5.68%, 5.69%, 4.64%, 4.90%, 6.35%, 3.56%, and the average relative error is 5.37%, both of which are less than 10%, proving that the improved BP neural network prediction model has good prediction accuracy.
Databáze: OpenAIRE