Study on a SVM-based data fusion method

Abstract

A new two-stage SVM-based data fusion strategy is proposed and it is applied to obtain the accurate information of the robot gripper state. Support vector machines (SVM) operate on the principle of structure risk minimization which not only keeps the empirical risk minimal but also control VC confidence of discriminate functions, hence better generalization ability is guaranteed. In this paper, the basic principles of SVM are discussed first and then a classified and graded data fusion strategy is proposed according to the features of the problem of gripper information data fusion. Finally, experimental results demonstrate the advantages and efficiency of the proposed approach.