基于GA-BP神经网络的柴油喷雾贯穿距预测

摘要

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.%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.