计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2009年
31期
134-137
,共4页
自组织映射%神经网络%分类%聚类%储粮害虫
自組織映射%神經網絡%分類%聚類%儲糧害蟲
자조직영사%신경망락%분류%취류%저량해충
self-organizing map%neural network classification%clustering%stored-grain pests
为了提高自组织特征映射(SOM)神经网络学习速度及分类精度,对初始连接权值及竞争层神经元数的确定方法进行改进.提出用聚类方法确定初始权值的新方法,还提出了采用聚类数与邻域之和确定竞争层神经元数的方法.并给出了改进后的SOM分类算法.将改进的SOM网络用于储粮害虫分类,采用留一方法进行分类验证实验.仿真结果表明,改进后的SOM网络在学习速度和分类精度方面都有明显提高,证明了该方法的有效性.
為瞭提高自組織特徵映射(SOM)神經網絡學習速度及分類精度,對初始連接權值及競爭層神經元數的確定方法進行改進.提齣用聚類方法確定初始權值的新方法,還提齣瞭採用聚類數與鄰域之和確定競爭層神經元數的方法.併給齣瞭改進後的SOM分類算法.將改進的SOM網絡用于儲糧害蟲分類,採用留一方法進行分類驗證實驗.倣真結果錶明,改進後的SOM網絡在學習速度和分類精度方麵都有明顯提高,證明瞭該方法的有效性.
위료제고자조직특정영사(SOM)신경망락학습속도급분류정도,대초시련접권치급경쟁층신경원수적학정방법진행개진.제출용취류방법학정초시권치적신방법,환제출료채용취류수여린역지화학정경쟁층신경원수적방법.병급출료개진후적SOM분류산법.장개진적SOM망락용우저량해충분류,채용류일방법진행분류험증실험.방진결과표명,개진후적SOM망락재학습속도화분류정도방면도유명현제고,증명료해방법적유효성.
In order to increase the learning speed and enhance the classification accuracy of SOM network,modify existing methods of determining the initial connection weights and the numbers of competitive layer nodes.Clustering method is proposed to determine the initial connection weights.A new method is proposed to determine the numbers of competitive layer nodes by adding up the cluster numbers and the numbers of the neighborhood neurons.And then the improved classification algorithm based on SOM network is presented.Apply the improved SOM network to classify stored-grain pests,and use leave-one-out method to train and test the network.The experimental results show that the modified SOM network has been markedly improved in learning speed and classification accuracy,which can prove the validity of the proposed methods.
