Title :
A new method of multi-sensor information fusion based on SVM
Author :
Li, Zhi-xin ; Ma, Yong-guang
Author_Institution :
Dept. of Control Sci. & Eng., North China Electr. Power Univ., Baoding, China
Abstract :
The support vector machine (SVM) is an algorithm based on structure risk minimizing principle, having high generalization ability. In the course of multi-sensor information fusion of industrial control, sensor has bigger nonlinearity and fuzzy relation between coefficient and relevant parameter. A kind of model and algorithm of multiple sensor information fusion based on the support vector machine are proposed. The model offered a kind of effective way for little sample, non-linear, high dimension.
Keywords :
regression analysis; sensor fusion; support vector machines; SVM; industrial control; multisensor information fusion; structure risk minimizing principle; support vector machine; Cybernetics; Industrial control; Information processing; Machine learning; Machine learning algorithms; Redundancy; Sensor fusion; Sensor phenomena and characterization; Sensor systems; Support vector machines; Information fusion; Regression function; SVM; Sample; Sensor;
Conference_Titel :
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location :
Baoding
Print_ISBN :
978-1-4244-3702-3
Electronic_ISBN :
978-1-4244-3703-0
DOI :
10.1109/ICMLC.2009.5212442
