Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method\nfor support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the\nmixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients,\nkernel function parameters, and regression parameters are combined together as the parameters of the state vector.Thus, themodel\nselection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter\nto estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the\nkernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF) support\nvector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better\ngeneralization ability and higher prediction accuracy.
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