caa a22 4500 606160310 CHVBK 20210128100629.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s00521-014-1798-3 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1798-3 Optimized support vector regression model by improved gravitational search algorithm for flatness pattern recognition [Elektronische Daten] [Peifeng Niu, Chao Liu, Pengfei Li, Guoqiang Li] 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. The Natural Computing Applications Forum, 2014 Flatness pattern recognition nationallicence Support vector regression nationallicence Improved gravitational search algorithm nationallicence Benchmark functions nationallicence Optimize nationallicence Niu Peifeng Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, 066004, Qinhuangdao, China aut Liu Chao Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, 066004, Qinhuangdao, China aut Li Pengfei Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, 066004, Qinhuangdao, China aut Li Guoqiang Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, 066004, Qinhuangdao, China aut Neural Computing and Applications Springer London 26/5(2015-07-01), 1167-1177 0941-0643 26:5<1167 2015 26 521 https://doi.org/10.1007/s00521-014-1798-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1798-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Niu Peifeng Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, 066004, Qinhuangdao, China aut NATIONALLICENCE 700 1- Liu Chao Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, 066004, Qinhuangdao, China aut NATIONALLICENCE 700 1- Li Pengfei Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, 066004, Qinhuangdao, China aut NATIONALLICENCE 700 1- Li Guoqiang Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, 066004, Qinhuangdao, China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/5(2015-07-01), 1167-1177 0941-0643 26:5<1167 2015 26 521