陕西师范大学学报：自然科学版
陝西師範大學學報：自然科學版
협서사범대학학보：자연과학판
Journal of Shaanxi Normal University： Nat Sci Ed
2012年
5期
31-34
,共4页
玉米碰撞声%特征%主成分分析%BP神经网络%分类
玉米踫撞聲%特徵%主成分分析%BP神經網絡%分類
옥미팽당성%특정%주성분분석%BP신경망락%분류
corn kernel impact acoustic signal%feature%principal component analysis%BP neural network%classification
为实现对玉米颗粒的自动分类,利用碰撞声信号装置采集玉米完好粒、虫蛀粒和霉变粒的840个声信号.分别从时域和频域对碰撞声信号进行分析和处理,提取信号特征.采用主成分分析方法对特征数据降维,利用BP神经网络进行分类.实验结果表明：该方法对完好粒、虫蛀粒和霉变粒3种玉米颗粒分类的正确率均达到90%以上.表明利用碰撞声信号识别玉米完好粒、虫蛀粒和霉变粒的效果良好,具有较强的实际应用价值,为检测玉米颗粒品质提供了一种新的途径.
為實現對玉米顆粒的自動分類,利用踫撞聲信號裝置採集玉米完好粒、蟲蛀粒和黴變粒的840箇聲信號.分彆從時域和頻域對踫撞聲信號進行分析和處理,提取信號特徵.採用主成分分析方法對特徵數據降維,利用BP神經網絡進行分類.實驗結果錶明：該方法對完好粒、蟲蛀粒和黴變粒3種玉米顆粒分類的正確率均達到90%以上.錶明利用踫撞聲信號識彆玉米完好粒、蟲蛀粒和黴變粒的效果良好,具有較彊的實際應用價值,為檢測玉米顆粒品質提供瞭一種新的途徑.
위실현대옥미과립적자동분류,이용팽당성신호장치채집옥미완호립、충주립화매변립적840개성신호.분별종시역화빈역대팽당성신호진행분석화처리,제취신호특정.채용주성분분석방법대특정수거강유,이용BP신경망락진행분류.실험결과표명：해방법대완호립、충주립화매변립3충옥미과립분류적정학솔균체도90%이상.표명이용팽당성신호식별옥미완호립、충주립화매변립적효과량호,구유교강적실제응용개치,위검측옥미과립품질제공료일충신적도경.
In order to realize the automatic classification of corn kernels,this approach collected 840 impact acoustic signals of undamaged kernels,insect damaged kernels and moldy kernels by apparatus of collecting impact acoustic signal,analyzed these signals from the time and frequency domain,extracted the signal features,used the principal component analysis method to reduce the dimensions of the feature data.Finally,BP neural network is used to classify the corn kernels.The classification accuracy of undamaged kernels,insect damaged kernels and moldy kernels were above 90%.The experimental results show that using impact acoustic signal,one can gain a good result in identifying undamaged kernels,insect damaged kernels and moldy kernels.So the approach has a more comprehensive value in practical application and provides a new method for corn kernels quality detection.
