Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

• Published in: Journal of control science and engineering Vol. 2017; no. 2017; pp. 1 - 12
• Main Authors: Wang, Hailun, Xu, Daxing
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2017; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Accuracy; Activated carbon; Adaptive filters; Artificial intelligence; Automation; Engineering; Fault diagnosis; Genetic algorithms; International conferences; Kalman filters; Kernel functions; Mathematical models; Methods; Nonlinear systems; Parameter estimation; Parameter identification; Principal components analysis; Regression; State estimation; Support vector machines; China
• ISSN: 1687-5249; 1687-5257
• DOI: 10.1155/2017/3614790

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