摘要
利用改进的BP神经网络算法,建立了样本柴油机排气温度的神经网络模型,通过柴油机台架实验采集柴油机转速、负荷、油耗、排气温度等参数作为神经网络模型学习样本,使用实验数据对所建立模型进行训练,并对该神经网络模型进行了误差分析,结果表明,所建神经网络模型反映了实验样机的排气温度变化规律,在测试数据范围内,排气温度辨识误差小于1.0%,满足计算要求.最后将神经网络预测模型与模糊推理结合,实现了柴油机排气再循环温度的智能控制.
In this paper,first,a neural network model describing the exhaust temperature of a diesel engine sample is established based on the improved BP neural network algorithm.Next,some data of engine speed,engine power,fuel consumption and exhaust temperature are obtained from beach tests,which are then used to train the established model.Finally,an error analysis is performed to verify the model.The results indicate that the established neural network model well describes the variation of exhaust temperature,and that the errors of the identification results,which are all less than 1%,meet the requirements of calculation.In addition,the intelligent temperature control of exhaust gas recirculation（EGR） is realized by combining the BP neural network model with the fuzzy inference.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第1期147-151,共5页
Journal of South China University of Technology(Natural Science Edition)
基金
广东省自然科学基金资助项目(B21B6070440)
华南理工大学SRP项目(Y1090110)
国家大学生创新实验项目(081056102)
关键词
BP神经网络
柴油机
排气温度
排气再循环
BP neural network
diesel engine
exhaust temperature
exhaust gas recirculation
