摘要
介绍了以提高工程车辆传动系效率为目的的自动换挡原理,以多尺度小波神经网络为基础构建了换挡模型,并利用自动变速控制实验数据对建立的模型进行验证性实验。实验结果表明,基于多尺度小波神经网络的换挡模型比遗传BP神经网络的换挡模型准确度更高,能更准确地实现换挡,更进一步提高了工程车辆传动系统的效率,达到了节约能源、增加效率的目的。
The automatic transmission principle was introduced to improve the efficiency of transmission system of construction vehicles,by using experimental data of automatic speed control to establish and verify the shift model based on multi-scale wavelet neural network.The results showed that the shift model based on multi-scale wavelet neural network can achieve higher accuracy than genetic BP neural network.It can further improve the efficiency of transmission system of construction vehicles and save energy.
出处
《四川大学学报(工程科学版)》
EI
CAS
CSCD
北大核心
2013年第2期188-192,共5页
Journal of Sichuan University (Engineering Science Edition)
基金
国家自然科学基金资助项目(50775096)
国家科技支撑计划资助项目(2013BAF07B00)
关键词
工程车辆
换挡原理
多尺度小波神经网络
construction vehicles
shift principle
multi-scale wavelet neural network