中南大学学报（自然科学版）2018，Vol.49Issue(1)：247-252,6.DOI:10.11817/j.issn.1672-7207.2018.01.031
基于GA-BP神经网络的柴油喷雾贯穿距预测
Prediction of diesel spray penetration length based on GA-BP neural network
摘要
Abstract
In order to solve the problem about measuring the penetration length of diesel spray, a prediction method based on GA-BP neural network was proposed in this work. Firstly, 30 sets of diesel spray penetration length were obtained by experiments under various environmental back pressures, injection pressures and injection pulse widths in a constant volume bomb. Then the first 20 sets and the last 10 sets were treated as training samples and test samples, respectively. Finally, BP and GA-BP neural network models were built and compared for the prediction of spray penetration length. The results show that the mean relative error and relative error variance of GA-BP neural network model are lower than those of the BP neural network model, and the number of iterations required for convergence is less than that of BP neural network model. The prediction model of diesel spray penetration length based on GA-BP neural network has higher accuracy and better performance, providing a low cost and high efficient method for measuring spray penetration length.关键词
BP神经网络/柴油喷雾/贯穿距/预测Key words
BP neural network/diesel spray/penetration length/prediction分类
能源科技引用本文复制引用
陈征,黎青青,肖乃松,吴诚,徐广辉,郝勇刚,刘长振..基于GA-BP神经网络的柴油喷雾贯穿距预测[J].中南大学学报（自然科学版）,2018,49(1):247-252,6.基金项目
中央高校基本科研业务费资助项目(227201401189) （227201401189）
内燃机燃烧学国家重点实验室开放基金资助项目(K2015-01) (Project(227201401189) supported by the Fundamental Research Funds for the Central Universities （K2015-01）
Project(K2015-01) supported by State Key Laboratory of Engines, Tianjin University) （K2015-01）
