Support Vector Machine and Its Application to System Identificatio

抄録

Support Vector Machines (SVMs) have become a subject of intensive study in statistical learning theory. They have been applied to successfully to classification problems and recently extended to regression problems. Support vector machines for regression problems is called Support Vector Regression (SVR). In this paper, a brief introduction to SVR is presented and then a new system identification method based on SVR is proposed for linear in parameter models. The effectiveness of the proposed method is examined through numerical examples.