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中南大学学报(自然科学版)

Journal of Central South University

第49卷    第1期    总第281期    2018年1月

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文章编号:1672-7207(2018)01-0247-06
基于GA-BP神经网络的柴油喷雾贯穿距预测
陈征1, 2,黎青青1, 2,肖乃松1, 2,吴诚1, 2,徐广辉3,郝勇刚3,刘长振3

(1. 汽车车身先进设计制造国家重点实验室,湖南 长沙,410082; 2. 湖南大学 机械与运载工程学院,湖南 长沙,410082; 3. 中国北方发动机研究所,天津,300380)

摘 要: 为解决柴油喷雾贯穿距测量的问题,提出一种基于GA-BP神经网络的预测方法。首先通过实验得到30组柴油在定容弹中不同环境背压、喷油压力和喷油脉宽等条件下的喷雾贯穿距,然后将前20组数据作为训练样本,后10组数据作为测试样本,最后分别通过BP神经网络和GA-BP神经网络建立喷雾贯穿距的预测模型。研究结果表明:GA-BP神经网络预测模型的平均相对误差和相对误差方差均比BP神经网络预测模型的低,并且其达到收敛时所需的迭代次数比BP神经网络预测模型的少。基于GA-BP神经网络的柴油喷雾贯穿距预测模型具有较高精度和适用性,为喷雾贯穿距的测量提供了一种低成本、高效率的方法。

 

关键字: BP神经网络;柴油喷雾;贯穿距;预测

Prediction of diesel spray penetration length based on GA-BP neural network
CHEN Zheng1, 2, LI Qingqing1, 2, XIAO Naisong1, 2, WU Cheng1, 2, XU Guanghui3, HAO Yonggang3, LIU Changzhen3

1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Changsha 410082, China; 2. College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China; 3. North China Institute of Engine, Tianjin 300380, China

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.

 

Key words: BP neural network; diesel spray; penetration length; prediction

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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