|本期目录/Table of Contents|

[1]林冬燕.GRNN神经网络在汽车发动机性能预测中的应用[J].集美大学学报(自然科学版),2023,28(5):467-472.
　LIN Dongyan.Application of GRNN Neural Network in Performance Prediction of Automotive Engine[J].Journal of Jimei University,2023,28(5):467-472.
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GRNN神经网络在汽车发动机性能预测中的应用(PDF)
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《集美大学学报（自然科学版）》[ISSN:1007-7405/CN:35-1186/N]

卷:
第28卷
期数:
2023年第5期
页码:
467-472
栏目:
数理科学与信息工程
出版日期:
2023-09-28

文章信息/Info

Title:
Application of GRNN Neural Network in Performance Prediction of Automotive Engine
作者:
林冬燕
集美大学海洋装备与机械工程学院，福建厦门 361021
Author(s):
LIN Dongyan
College of Marine Equipment and Mechanical Engineering,Jimei University,Xiamen 361021,China
关键词:
汽车发动机; 预测模型; 广义回归神经网络; 动力性能; 燃油消耗率
Keywords:
automotive engine; prediction model; generalized regression neural network (GRNN); power performance; fuel consumption rate
分类号:
-
DOI:
-
文献标志码:
A
摘要:
建立多输入参数条件下发动机动力性能及燃油经济性能预测模型，研究平滑因子、输入参数对预测精度的影响；建立预测模型，研究发动机运转参数对动力性能与燃油消耗率的影响规律。研究结果表明：采用广义回归神经网络（GRNN）能构建准确性较高的发动机动力性能与燃油经济性能预测模型；选择合适的平滑因子可使GRNN算法获得的预测值避免出现较大波动，同时兼顾较高预测精度；保持合适的油门开度能使发动机输出较高的功率和转矩；低功率或低油门开度使发动机燃油消耗率较高。
Abstract:
The prediction models of engine power performance and fuel economy under multi input parameters are established. The influence of smoothing factors and input parameters on prediction accuracy has been studied. The influence of engine operating parameters on power performance and fuel consumption rate was studied through the established prediction modes.The results show that the generalized regression neural network (GRNN) can be used to build a more accurate prediction model of engine power performance and fuel economy. The appropriate smoothing factor can not only avoid large fluctuations in the predicted value of GRNN,but also achieve high prediction accuracy. Engine can output higher power and torque under the appropriate throttle opening conditions. The engine fuel consumption rate has a high value at low power or low throttle opening.

参考文献/References:

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　LI Meng-jie,HUANG Jia-liang.Prediction of NOxEmissions from Marine Diesel Engine Based on RBF Neural Network[J].Journal of Jimei University,2016,21(5):136.
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备注/Memo

备注/Memo:
更新日期/Last Update: 2024-01-05