The Application Of Support Vector Machine In Cable Fault Classification

Support vector machine is a pattern recognition method specially for small samples, which is based on the VC dimensions of the statistical learning theory and the structural risk minimization principle. It well sloves the problems of nonlinear, high dimensions and local minimum. It has a good generalization performance. The classical support vector machine is a two classs classifiers. With the expansion of its application,it is widely used to solve the multi-class classification problems. For a special problem, how to build a better support vector machine multi-classification model has become one of the spots of support vector machines in recent years.The multi-class support vector machine algorithm and the selecting problem of kernel parameters are analyzed and researched in the thesis based on the statistical learning theory and the support vector machine theory. First, the statistical learning theory, the support vector machine classification principle and the techniques which the support vector machine has integrates including the optimal hyperplane, the classification interval, the optimization theory are summarized. At the same time, the kernel function, which is an important module of the support vector machine is described and the performance of differnt kernel functions are compared. Then, several existing multi-class support vector algorithms including the one-time algorithm, one versus rest algorithm, one versus one algorithm and the acyclic graph algorithm are analyzed and their performance, advantages and disadvantages are compared. Aming at the problem that there exsits some wrongly classified region or refused region in the above algorithms and the difficulty to determine the structure of the binary tree in the binary tree support vector machine algorithm, a support vector machine algorithm based on the between-class dissimilarity matrix is presented after the two novel concepts of the shorter neighboring distance and the between-class dissimilarity matrix are defined. In the simulation, the results of the new algorithm are compared with that of one versus one algorithm, one versus rest algorithm and the acyclic graph algorithm, which verifies that the alogrithm is correct and effective.In the last part of the thesis, the support vector machine algorithm based on the between-class dissimilairity matrix is applied to the recognition for the types of cable fault. The fault types are finally effectively recognized after selecting a better parameter by trying differernt kernel parameters to build the multi-class support vector machine model.