关键词: 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）

Abstract: 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 dimensional data employing disturbance?BP network?can greatly improve the?classification accuracy and?training speed?of data.

Key words: high-dimension data, neural network, Back Propagation（BP） algorithm, high-order differential, perturbed Back Propagation（BP）