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基于降维BP神经网络的高维数据分类研究
引用本文: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,2013,49(20):183-187.
作者姓名:康辉英  李明亮
作者单位:1.石家庄经济学院 华信学院,石家庄 050031 2.石家庄经济学院 河北省光电信息与地球探测技术重点实验室,石家庄 050031 3.石家庄经济学院 信息工程学院,石家庄 050031
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)。
摘    要: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。

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)  

High-dimensional data classification based on dimension reduction of BP neural network
KANG Huiying , LI Mingliang.High-dimensional data classification based on dimension reduction of BP neural network[J].Computer Engineering and Applications,2013,49(20):183-187.
Authors:KANG Huiying  LI Mingliang
Affiliation:1.College of Huaxin, Shijiazhuang University of Economics, Shijiazhuang 050031, China 2.Hebei Key Laboratory of Optoelectronic Information and Geo-detection Technology, Shijiazhuang University of Economics, Shijiazhuang 050031, China 3.College of Information Technology, Shijiazhuang University of Economics, Shijiazhuang 050031, China
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.
Keywords:high-dimension data  neural network  Back Propagation(BP)algorithm  high-order differential  perturbed Back Propagation(BP)
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