板形模式识别的多输出最小二乘支持向量回归机新方法  

A Novel Method for Flatness Pattern Recognition via MLSSVR

作　　者：张秀玲[1,2] 张少宇[1] 赵文保[1] 徐腾[1] 

机构地区：[1]燕山大学河北省工业计算机控制工程重点实验室,秦皇岛066004 [2]国家冷轧板带装备及工艺工程技术研究中心,秦皇岛066004

出　　处：《中国机械工程》2013年第2期258-263,共6页China Mechanical Engineering

基　　金：国家自然科学基金资助项目(50675186)

摘　　要：为了克服最小二乘支持向量回归机(LS-SVR)算法不能直接应用于多输入多输出(MIMO)系统建模的缺点,通过在目标函数中加入样本绝对误差项,提出了一种多输出最小二乘支持向量回归机(MLSSVR)新算法。将MLSSVR算法应用于板形模式识别研究,提出了一种基于MLSSVR的板形模式识别新方法,将该方法与LS-SVR合成识别方法进行对比实验,并对MLSSVR识别模型的识别能力进行了测试和分析,结果证明了MLSSVR算法的有效性。MLSSVR板形模式识别方法不仅避免了LS-SVR合成方法的复杂组合运算,具有更高的识别速度,而且具有更高精度和很强的泛化能力。In order to overcome the disadvantages that LS--SVR algorithm is not suitable to multiple input multiple output system modeling directly,a novel algorithm defined as MLSSVR was proposed by adding sample absolute errors in objective function. And a novel flatness pattern recognition method based on MLSSVR was put forward by applying MLSSVR algorithm on pattern recognition. Then,comparison between the MLSSVR recognition method and the combination method of LS- SVR was conducted, and the recognition ability of MLSSVR recognition model was tested and analyzed. Experimental results demonstrate the validity of the MLSSVR algorithm. The flatness pattern recognition model based on MLSSVR can avoid complex computation of LS--SVR combination method, enhance the recognition speed effectively, and has higher recognition accuracy and good generalization ability.

关 键 词：最小二乘支持向量回归机 多输出最小二乘支持向量回归机 板形 模式识别 

分 类 号：TG335.5[金属学及工艺—金属压力加工] TP181[自动化与计算机技术—控制理论与控制工程]