Abstract
Support Vector Machine (SVM) is originally designed for linear binary pattern recognition. According to defect of common SVM, the Least square SVM is discussed to classify the small samples. In fault diagnosis, multi-category faults are common. Many multi-category SVMs are introduced in this paper briefly, and then an immune guided decision tree SVM (DTSVM) is presented which takes into account the fault occurring frequency. It proposes a fault isolation algorithm which adopts immune memory to optimize the configuration of DTSVM. Simulation and examples show that this method is useful in fault isolation. The kernel functions play an important role in the classification. The choice of kernel function and its parameters is neither easy nor trivial. In this paper, we have researched the kernel function of RBF and its effect on classification.
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