亚洲一区欧美在线,日韩欧美视频免费观看,色戒的三场床戏分别是在几段,欧美日韩国产在线人成

多類分類SVM在工程車輛自動(dòng)變速擋位決策中的應(yīng)用
作者:
作者單位:

作者簡(jiǎn)介:

通訊作者:

中圖分類號(hào):

基金項(xiàng)目:


Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 圖/表
  • |
  • 訪問(wèn)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(píng)論
    摘要:

    經(jīng)典的支持向量機(jī)(SVM)是針對(duì)二類分類的,在解決工程車輛自動(dòng)變速擋位決策這種典型的多類分類問(wèn)題時(shí)存在困難。本文提出了基于二叉數(shù)支持向量機(jī)的擋位決策算法,將分類器分布在各個(gè)節(jié)點(diǎn)上,從而構(gòu)成了多類分類支持向量機(jī),減少了分類器數(shù)量和重復(fù)訓(xùn)練樣本的數(shù)量。該方法能夠根據(jù)車輛的運(yùn)行狀態(tài)確定最佳擋位,從而及時(shí)、準(zhǔn)確地滿足工程車輛自動(dòng)換擋的要求。試驗(yàn)結(jié)果表明:基于二叉樹(shù)的支持向量機(jī)性能要比遺傳RBF神經(jīng)網(wǎng)絡(luò)略好。

    Abstract:

    The traditional support vector machines only deals with the binary classification. It has difficulty in solving the multi-class classification problem like the shift decision for the automatic transmission of the engineering vehicle. A shift decision algorithm that based on SVM-binary tree was presented. This method distributed classifier to nodes that constituted multi-class SVM. The number of SVM classifier and duplicated training samples could be reduced. The optimal shifting gear could be decided by the proposed approach, and the requirement of the engineering vehicle to the automatic shifting could be satisfied in time and accurately. The experiment showed that the support vector machines based on binary tree achieved better results than RBF neural network with genetics.

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

韓順杰,趙丁選.多類分類SVM在工程車輛自動(dòng)變速擋位決策中的應(yīng)用[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2007,38(2):10-12.[J]. Transactions of the Chinese Society for Agricultural Machinery,2007,38(2):10-12.

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: