• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>DSC-vol.73-1; American Society of Mechanical Engineers(ASME) International Mechanical Engineering Congress and Exposition pt.A; 20041113-19; Anaheim,CA(US) >EXPERIMENTAL VALIDATION OF A RECURRENT NEURAL NETWORK FOR AIR-FUEL RATIO DYNAMIC SIMULATION IN S.I. I.C. ENGINES
【24h】

EXPERIMENTAL VALIDATION OF A RECURRENT NEURAL NETWORK FOR AIR-FUEL RATIO DYNAMIC SIMULATION IN S.I. I.C. ENGINES

机译：S.I.C.中空燃比动态仿真的递归神经网络的实验验证引擎

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The paper deals with the simulation of the wall wetting dynamics in SI engines, making use of Recurrent Neural Networks (RNN). RNN are derived from the Multi Layer Feedforward Neural Networks, largely adopted for static mapping, by considering feedback connections between output and input layers. A Multi Input-Single Output structure has been adopted, assuming injected fuel, manifold pressure and engine speed as external input variables; the Air-Fuel Ratio at the exhaust gas oxygen sensor location has been considered as system output. The RNN has been trained (i.e. identified) and tested vs. a set of transient data measured on a commercial 4 cylinders SI engine at the test bench. The results show a good level of accuracy confirming the suitability of RNN for both HIL simulation or off-line identification of classical Mean Value Models with a drastic reduction of the calibration effort.
机译：本文利用递归神经网络（RNN）来模拟SI发动机中的壁润湿动力学。 RNN是从多层前馈神经网络派生而来的，它考虑了输出层和输入层之间的反馈连接，因此广泛用于静态映射。采用多输入-单输出结构，假定喷射的燃油，歧管压力和发动机转速为外部输入变量；排气氧传感器位置的空燃比已被视为系统输出。已对RNN进行了训练（即确定）并进行了测试，并对比了在试验台上使用商用4缸SI发动机测量的一组瞬态数据。结果显示出很高的准确性，证实了RNN适用于HIL模拟或经典均值模型的离线识别，并且大大减少了校准工作。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号