计算机工程与应用Issue(20)：183-187,5.DOI:10.3778/j.issn.1002-8331.1201-0137
基于降维BP神经网络的高维数据分类研究
High-dimensional data classification based on dimension reduction of BP neural network
摘要
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 dimen-sional data employing disturbance BP network can greatly improve the classification accuracy and training speed of data.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)Key words
high-dimension data/neural network/Back Propagation(BP)algorithm/high-order differential/perturbed Back Propagation(BP)分类
信息技术与安全科学引用本文复制引用
康辉英,李明亮..基于降维BP神经网络的高维数据分类研究[J].计算机工程与应用,2013,(20):183-187,5.基金项目
国家自然科学基金（No.51077125/E070602）；中国科技部科技人员服务企业行动项目基金（No.2009GJA0035）。 （）
