Optimized support vector regression model by improved gravitational search algorithm for flatness pattern recognition

Accurately, forecasting of the flatness plays a highly significant role in the flatness theory and flatness control system, but it is quite difficult and complicated due to the nonlinear characteristics of flatness pattern recognition and lack of available observed data set. Recently, support vector regression (SVR) is being proved an effective machine learning technique for solving nonlinear regression problem with small sample set, because of its nonlinear mapping capabilities. However, it has also been proved that the prediction precision of SVR is highly dependent of SVR parameters, which are hardly choosing for the SVR. As in many excellent algorithms, gravitational search algorithm (GSA) not only has strong global searching capability, but also is very easy to implement. In the paper, an improved gravitational search algorithm (IGSA) is presented to further enhance optimal performance of GSA, and it is employed to serve as a method for pre-selecting SVR parameters. Based on SVR and IGSA algorithms, a forecasting model of flatness pattern recognition is proposed. Where, the IGSA is employed to optimize the parameters of SVR model to determine the parameters as fast and accurate as possible. Afterward, a procedure of forecasting flatness was put forward to evaluate the efficiency of the proposed IGSA–SVR model, which was compared with normal SVR model, IGSA–BP model and extreme learning machine model. The results affirm that the proposed algorithm outperforms other technique.