Improved Method of Electrical Capacitance Tomography Based on SVM Algorithm of Cyclic Symmetrical Partition

According to support vector machine( SVM) has low accuracy and low training speed to deal with large scale sample matrix in ECT system,a new algorithm that combined SVM with the cyclic symmetrical partition( CSPSVM) is presented. By the cyclic symmetry of ECT system model,a large sample matrix is simplified according to a layer of the imaging unit,and segments block selectively into multiple smaller sample matrixes. Then they are trained by SVM respectively,and the decision function obtained can be used to classify the prediction sample.Finally,all prediction units are combined for imaging. Experimental results show that the image reconstruction using CSPSVM algorithm has higher classification accuracy and shorter imaging time than using SVM alone.