针对目前板形模式识别模型泛化能力不高、训练速度慢等缺陷,以1次、2次、3次、4次勒让德正交多项式为板形缺陷基本模式,提出了由支持向量回归机（SVR）构建的模式识别模型;为了提高该模型的精确度,引入万有引力算法（GSA）优化SVR的参数,由此构成GSA-SVR预测模型。仿真试验结果表明：GSA-SVR模型不仅识别结果精度高,而且与PSO-BP神经网络模型相比泛化能力更强,训练速度更快,其识别结果可以为板形控制提供有效的依据。
In the light of the problems existed in flatness pattern recognition models which had low generation ability and slow training speed,a new model was proposed.The model took linear,quadratic,cubic and biquadrate Legendre orthogonal polynomials as flatness basic patterns and was constituted by support vector regression(SVR).In order to improve the precision of the model,gravitational search algorithm(GSA) was brought in.So the GSA-SVR model was completed.The simulation experiments indicate that the model's(GSA-SVR) recognition result is more precise than PSO-BP model,and it has strong generalization ability and fast training speed.The result of GSA-SVR model can provide reliable basis for the formulation of flatness control strategy.
参考文献
| [1] | JIA Chun-yu,SHAN Xiu-ying,LIU Hong-min,NIU Zhao-ping.Fuzzy Neural Model for Flatness Pattern Recognition[J].钢铁研究学报(英文版),2008(06):33-38. |
| [2] | Chang Chih-cheag;Chien Li-jen;Lee Yuh-jye .A Novel Framework for Multi-Class Classification via Ternary Smooth Support Vector Machine[J].Pattern Recognition,2011,44:1235. |
| [3] | Peng Xinjun .TPMSVM:A Aovel Twin Parametric-Margin Support Vector Machine for Pattern Reeognition[J].Pattern Recognition,2011,44:2678. |
| [4] | Yang Z;Gu X S .Genetic Algorithm-Least Squares Support Vector Regression Based Predicting and Optimizing Model on Carbon Fiber Composite Integrated Conductiyity[J].Materi-als and Design,2010,31:1042. |
| [5] | 张雪伟 .基于智能方法的板形识别与控制[D].济南大学,2010. |
| [6] | 张春霞,吴良.基于支持向量机的不锈钢冷轧薄板力学性能的预测模型[J].东华大学学报（自然科学版）,2010(05):559-562. |
| [7] | Yan Peng;Hongmin Liu;R. Du .A neural network-based shape control system for cold rolling operations[J].Journal of Materials Processing Technology,2008(1/3):54-60. |
| [8] | 单修迎,刘宏民,贾春玉.含有三次板形的新型板形模式识别方法[J].钢铁,2010(08):56-60. |
| [9] | 邓乃扬.数据挖掘中的新方法:支持向量机[M].北京:科学出版社,2004 |
| [10] | Esmat Rashedi;Hossein Nezamabadi-pour;Saeid Saryazdl.GSA .A Gravitational Search Algorithm[J].Information Sci-ences,2009,179:2232. |
| [11] | Sarafrazi S;Nezamabadi-pour H;Saryazdi S .Disruption:A New Operator in Gravitational Search Algorithm[J].Scientia Iranica D,2011,18(03):539. |
| [12] | 张秀玲,刘宏民.板形模式识别的GA-BP模型和改进的最小二乘法[J].钢铁,2003(10):29-34. |
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