A Novel Method for Flatness Pattern Recognition via Least Squares Support Vector Regression

查看全文 作　　者：ZHANG Xiu-ling, ZHANG Shao-yu, TAN Guang-zhong, ZHAO Wen-bao (Key Laboratory of Industrial Computer Control Engineering of Hebei Province, National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinhuangdao 066004, Hebei, China)

出　　处：《Journal of Iron and Steel Research(International)》索引2012年第19卷第3期,共6页

基　　金：Sponsored by National Natural Science Foundation of China (50675186) 摘　　要：To adapt to the new requirement of the developing flatness control theory and technology, cubic patterns were introduced on the basis of the traditional linear, quadratic and quartic flatness basic patterns. Linear, quadratic, cubic and quartic Legendre orthogonal polynomials were adopted to express the flatness basic patterns. In order to overcome the defects live in the existent recognition methods based on fuzzy, neural network and support vector regression (SVR) theory, a novel flatness pattern recognition method based on least squares support vector regression (LS-SVR) was proposed. On this basis, for the purpose of determining the hyper-parameters of LS-SVR effectively and enhancing the recognition accuracy and generalization performance of the model, particle swarm optimization algorithm with leave-one-out (LOO) error as fitness function was adopted. To overcome the disadvantage of high computational complexity of naive cross-validation algorithm, a novel fast cross-validation algorithm was introduced to calculate the LOO error of LS-SVR. Results of experiments on flatness data calculated by theory and a 900HC cold-rolling mill practically measured flatness signals demonstrate that the proposed approach can distinguish the types and define the magnitudes of the flatness defects effectively with high accuracy, high speed and strong generalization ability. 关键词：支持向量回归模式识别方法最小二乘板形 LEGENDRE 粒子群优化算法计算复杂性识别精度

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A Novel Method for Flatness Pattern Recognition via Least Squares Support Vector Regression

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耦合文献(3371)

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