计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2013年
20期
183-187
,共5页
高维数据%神经网络%反向传播(BP)算法%高阶微分%扰动反向传播(BP)
高維數據%神經網絡%反嚮傳播(BP)算法%高階微分%擾動反嚮傳播(BP)
고유수거%신경망락%반향전파(BP)산법%고계미분%우동반향전파(BP)
high-dimension data%neural network%Back Propagation(BP)algorithm%high-order differential%perturbed Back Propagation(BP)
为确保高维数据的神经网络分类精度，提出了先降维后分类的方法。采用主成分分析（PCA）法实现高维数据的降维。通过分析传统BP算法，提出分两步来更新网络权值的扰动BP学习方法。采用MATLAB对降维分类算法的分类精度和误差收敛速度进行分析。仿真结果显示：先降维再采用扰动BP网络进行高维数据分类可大大提高数据的分类精度和训练速度。
為確保高維數據的神經網絡分類精度，提齣瞭先降維後分類的方法。採用主成分分析（PCA）法實現高維數據的降維。通過分析傳統BP算法，提齣分兩步來更新網絡權值的擾動BP學習方法。採用MATLAB對降維分類算法的分類精度和誤差收斂速度進行分析。倣真結果顯示：先降維再採用擾動BP網絡進行高維數據分類可大大提高數據的分類精度和訓練速度。
위학보고유수거적신경망락분류정도，제출료선강유후분류적방법。채용주성분분석（PCA）법실현고유수거적강유。통과분석전통BP산법，제출분량보래경신망락권치적우동BP학습방법。채용MATLAB대강유분류산법적분류정도화오차수렴속도진행분석。방진결과현시：선강유재채용우동BP망락진행고유수거분류가대대제고수거적분류정도화훈련속도。
To ensure the classification accuracy of the neural network of high-dimensional data, it proposes to firstly reduce its dimension and then to do classification. And it in fact achieves the dimension reduction of high-dimensional data by Principal Component Analysis(PCA). By analysis of the traditional BP algorithm, the proposed disturbance BP learning method is divided into two steps to update the network weights. It analyzes the classification accuracy and error convergence rate of the algorithm through MATLAB. The simulation results show that firstly reducing its dimension and then doing classification of high dimen-sional data employing disturbance BP network can greatly improve the classification accuracy and training speed of data.
