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多類分類SVM在工程車輛自動變速擋位決策中的應(yīng)用
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    摘要:

    經(jīng)典的支持向量機(jī)(SVM)是針對二類分類的,,在解決工程車輛自動變速擋位決策這種典型的多類分類問題時(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換擋的要求,。試驗(yàn)結(jié)果表明:基于二叉樹的支持向量機(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.

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韓順杰,趙丁選.多類分類SVM在工程車輛自動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.

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