Zhang Zhousuo,Li Lingjun,He Zhengjia
(School of Mechanical Engineering,Xi'an Jiaotong University,Xi'an 710049, China)
Abstract:It is significant to choose kernel function and its optimization parameters for performance of suport vector machine. Aiming at parameter optimization of fault classifier based on support vector machine, the principle of parameter optimization of support vector machine by means of minimizing the radius-margin (RM) upper bound as optimum object was discussed. Then a simplified algorithm was proposed. The algorithm does not require computing the gradient and can optimize one parameter of kernel function by adopting constant iterative step length. Based on the algorithm, parameter optimization of binary fault classifier was implemented. The simplified algorithm was applied to fault classifier which classifies steam oscillation fault and bearing bushing looseness fault of turboª²generator set. Testing results show that the classification capability of the fault classifier can be improved by means of the parameter optimization algorithm.
Keywords:support vector machine£»fault classifier£»parameter optimization
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