ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

Support vector regression (SVR) is an effective tool for machine error modeling. To improve the predicted performance of SVR model, the core vector regression (CVR) algorithm which is suitable for resolving the training of large-scale sample data was introduced into thermal error modeling for CNC machine tool. Principal components were firstly extracted from the sample set using the feature extraction of partial least squares (PLS) algorithm to construct the feature set, which would reduce the number of state variables without information loss by dimension reduction, data de-noising and eliminating the correlative between variables. Then improved particle swam optimization (IPSO) was applied for determining the parameters of CVR to get the optimal performance of the thermal error model, and the proposed combined method was called PLS—IPSO—CVR. Experimental results showed that the training speed of PLS—IPSO—CVR model was much faster and it produced fewer support vectors on very large sample data in comparison with SVR and BP neural network. Thus the feasibility and effectiveness of this combined modeling method was verified.