板形模式识别的多输出最小二乘支持向量回归机新方法OA北大核心CSCDCSTPCD
A Novel Method for Flatness Pattern Recognition via MLSSVR
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…查看全部>>
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 -…查看全部>>
张秀玲;张少宇;赵文保;徐腾
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国家冷轧板带装备及工艺工程技术研究中心,秦皇岛,066004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
矿业与冶金
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
least squares support vector regression(LS- SVR) multi - output least squares support vector regression(MLSSVR) flatnesspattern recognition
《中国机械工程》 2013 (2)
现代带钢冷轧机板形在线控制模糊神经模型的研究
258-263,6
国家自然科学基金资助项目(50675186)
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