ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

煤層氣發(fā)動機(jī)混合氣充量系數(shù)模型的辨識
CSTR:
作者:
作者單位:

作者簡介:

通訊作者:

中圖分類號:

基金項(xiàng)目:


Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 圖/表
  • |
  • 訪問統(tǒng)計(jì)
  • |
  • 參考文獻(xiàn)
  • |
  • 相似文獻(xiàn)
  • |
  • 引證文獻(xiàn)
  • |
  • 資源附件
  • |
  • 文章評論
    摘要:

    將充量系數(shù)作為預(yù)混合點(diǎn)燃式煤層氣發(fā)動機(jī)轉(zhuǎn)速和進(jìn)氣歧管壓力的函數(shù),,根據(jù)實(shí)驗(yàn)數(shù)據(jù),,應(yīng)用系統(tǒng)辨識方法,建立了基于多項(xiàng)式,、BP神經(jīng)網(wǎng)絡(luò)和自適應(yīng)神經(jīng)網(wǎng)絡(luò)模糊推理系統(tǒng)(ANFIS)的混合氣充量系數(shù)模型,,比較了各種模型的建模效果,。為了驗(yàn)證模型的有效性,將所建充量系數(shù)模型分別嵌入煤層氣發(fā)動機(jī)平均值模型,,對平均值模型的估計(jì)值和實(shí)驗(yàn)數(shù)據(jù)進(jìn)行了比較,。檢驗(yàn)結(jié)果表明,充量系數(shù)的非參數(shù)模型比多項(xiàng)式模型具有更高的預(yù)測精度,,適合作為系統(tǒng)仿真的子模型,。

    Abstract:

    The volumetric efficiency of gaseous mixture (VEGM) is regarded as a function of speed and intake manifold absolute pressure of a pre-mixed spark ignition coal-bed gas engine. Three models of VEGM were developed based on polynomial, BP neural network and the adaptive neural fuzzy inference system (ANFIS), respectively, by using the system identification method and the experimental data. The modeling efficiencies of various models was compared. In order to validate the models, the volumetric efficiency models were embedded into the mean value model of the coal-bed gas engine respectively. The estimated output of the mean value model was compared with the experiments data. The results show that non-parametric models of the volumetric efficiency are more accurate than the parametric model for prediction and more suitable for system simulation as a sub-model.

    參考文獻(xiàn)
    相似文獻(xiàn)
    引證文獻(xiàn)
引用本文

滕勤,楊瑜,左承基,談建.煤層氣發(fā)動機(jī)混合氣充量系數(shù)模型的辨識[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2007,38(3):47-51.[J]. Transactions of the Chinese Society for Agricultural Machinery,2007,38(3):47-51.

復(fù)制
分享
文章指標(biāo)
  • 點(diǎn)擊次數(shù):
  • 下載次數(shù):
  • HTML閱讀次數(shù):
  • 引用次數(shù):
歷史
  • 收稿日期:
  • 最后修改日期:
  • 錄用日期:
  • 在線發(fā)布日期:
  • 出版日期:
文章二維碼