计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
2期
151-155
,共5页
人工蜂群算法%支持向量机%参数优化%小麦碰撞声%分类
人工蜂群算法%支持嚮量機%參數優化%小麥踫撞聲%分類
인공봉군산법%지지향량궤%삼수우화%소맥팽당성%분류
artificial bee colony algorithm%Support Vector Machine(SVM)%parameter optimization%wheat impact acoustic signals%classification
为了提高支持向量机分类准确率，采用人工蜂群算法对支持向量机参数进行优化，并将该优化方法应用于小麦完好粒、霉变粒和发芽粒三类麦粒的识别。使用小波变换分解信号能量作为特征向量，以分类错误率的倒数作为适应度函数，利用人工蜂群算法对支持向量机的惩罚因子和核函数宽度参数进行优化，优化SVM方法对小麦完好粒、霉变粒和发芽粒的分类正确率达到86%以上。实验结果表明，该研究有较强的实用价值，为SVM性能优化提供了一种新的方法。
為瞭提高支持嚮量機分類準確率，採用人工蜂群算法對支持嚮量機參數進行優化，併將該優化方法應用于小麥完好粒、黴變粒和髮芽粒三類麥粒的識彆。使用小波變換分解信號能量作為特徵嚮量，以分類錯誤率的倒數作為適應度函數，利用人工蜂群算法對支持嚮量機的懲罰因子和覈函數寬度參數進行優化，優化SVM方法對小麥完好粒、黴變粒和髮芽粒的分類正確率達到86%以上。實驗結果錶明，該研究有較彊的實用價值，為SVM性能優化提供瞭一種新的方法。
위료제고지지향량궤분류준학솔，채용인공봉군산법대지지향량궤삼수진행우화，병장해우화방법응용우소맥완호립、매변립화발아립삼류맥립적식별。사용소파변환분해신호능량작위특정향량，이분류착오솔적도수작위괄응도함수，이용인공봉군산법대지지향량궤적징벌인자화핵함수관도삼수진행우화，우화SVM방법대소맥완호립、매변립화발아립적분류정학솔체도86%이상。실험결과표명，해연구유교강적실용개치，위SVM성능우화제공료일충신적방법。
In order to improve the classification precision of Support Vector Machines(SVM), a parameter optimization method based on artificial bee colony algorithm is proposed to solve this problem. Then the proposed method is applied to the recognition of undamaged wheat kernel, moldy damaged wheat kernel and sprout-damaged wheat kernel. The wavelet transform is applied to wheat impact acoustic signals and the normalized energy values of every frequency band are extracted to compose feature vectors. And the inverse of classification error rate is used as fitness value, and the artificial bee colony algorithm is used to optimize the penalty factor and kernel parameter of SVM. Then the optimized SVM is used to classify the undamaged kernel, moldy damaged kernel and sprout-damaged kernel, and the recognition accuracy rate is above 86%. The experimental results show that the research has a more universal value in application and provide a novel method for the optimization of SVM performance as well.
