Classification and Recognition Method of Information Based on Multi-source Measuring and Attributes Mixing
GUAN Xin
CHANG Jin
WANG Hong
YI Xiao
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 .
Keywords：principal component analysisnearest peak regulationoptimal separating planeBP neural network
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 615-620 )