Flexible Support Vector Regression and Its Application to Fault Detection

Hyper-parameters, which determine the ability of learning and generalization for support vector regression (SVR), are usually fixed during training. Thus when SVR is applied to complex system modeling, this parameters-fixed strategy leaves the SVR in a dilemma of selecting rigorous or slack parameters due to complicated distributions of sample dataset. Therefore in this paper we proposed a flexible support vector regression (F-SVR) in which parameters are adaptive to sample dataset distributions during training. The method F-SVR divides the training sample dataset into several domains according to the distribution complexity, and generates a different parameter set for each domain. The efficacy of the proposed method is validated on an artificial dataset, where F-SVR yields better generalization ability than conventional SVR methods while maintaining good learning ability. Finally, we also apply F-SVR successfully to practical fault detection of a high frequency power supply.