计算机与数字工程2016，Vol.44Issue(4)：615-620,6.DOI:10.3969/j.issn.1672-9722.2016.04.012
基于多源测量与属性混合信息的分类识别方法
Classification and Recognition Method of Information Based on Multi-source Measuring and Attributes Mixing
摘要
Abstract
Multi‐source and heterogeneous information provided by multisensor system is being utilized fully .The paper merges attributes whose observation data is at data level and somes at feature level into a feature vector that describes target . On the basis of principal component analysis of feature vector ,it is transformed to triangular rectangular‐coordinates system to find a optimal separating plane for classification and recognition .“One‐Against‐One” strategy is used to deal with the multi‐class problems .The validity of the method is validated by simulation experiments in the environment of different per ‐centages of gaussian white noise ,then this paper carrys on comparison experiment with BP neural network recognition meth ‐od in same condition .It shows the superiority of higher recognition rate ,faster recognition speed and higher stability of the proposed method .关键词
主成分分析/最近顶点规则/最优分类平面/BP 神经网络Key words
principal component analysis/nearest peak regulation/optimal separating plane/BP neural network分类
信息技术与安全科学引用本文复制引用
关欣,常进,王虹,衣晓..基于多源测量与属性混合信息的分类识别方法[J].计算机与数字工程,2016,44(4):615-620,6.基金项目
国家自然科学基金重点项目（编号61032001）；教育部新世纪优秀人才支持计划项目（编号NCET-11-0872）资助。 （）
