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改进组合分类器的冷轧带钢表面缺陷识别研究

化春键 周海英

化春键, 周海英. 改进组合分类器的冷轧带钢表面缺陷识别研究[J]. 机械科学与技术, 2017, 36(11): 1785-1790. doi: 10.13433/j.cnki.1003-8728.2017.1124
引用本文: 化春键, 周海英. 改进组合分类器的冷轧带钢表面缺陷识别研究[J]. 机械科学与技术, 2017, 36(11): 1785-1790. doi: 10.13433/j.cnki.1003-8728.2017.1124
Hua Chunjian, Zhou Haiying. Study on Surface Defect Recognition of Cold Rolled Steel Strip by Improving Combinationclassifier[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(11): 1785-1790. doi: 10.13433/j.cnki.1003-8728.2017.1124
Citation: Hua Chunjian, Zhou Haiying. Study on Surface Defect Recognition of Cold Rolled Steel Strip by Improving Combinationclassifier[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(11): 1785-1790. doi: 10.13433/j.cnki.1003-8728.2017.1124

改进组合分类器的冷轧带钢表面缺陷识别研究

doi: 10.13433/j.cnki.1003-8728.2017.1124
基金项目: 

国家自然科学基金项目(61104213)与中央高校基本科研业务费专项资金项目(JUSRP11008)资助

详细信息
    作者简介:

    化春键(1975-),副教授,博士,cjhua@jiangnan.edu.cn

Study on Surface Defect Recognition of Cold Rolled Steel Strip by Improving Combinationclassifier

  • 摘要: 针对表面缺陷种类多样、形态复杂的冷轧带钢,若采用单一分类器识别分类,会存在对个别缺陷不敏感、识别率低的情况,且会导致分类器处理特征数据规模过大,系统的鲁棒性和稳定性很难得到保证。为此提出基于改进组合分类器的冷轧带钢表面缺陷识别方法,将优化BP神经网络、概率神经网络以及改进的支持向量机进行组合,利用分类信息的互补性进行综合分类,从而构建了较优的分类系统。实验结果表明:改进组合分类器弥补了单个分类器网络训练的不足;针对每一类缺陷识别时准确率都较高,能增加整体分类器的泛化能力,整体识别正确率可达95%以上,且识别高效、稳定,具有实用价值。
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出版历程
  • 收稿日期:  2016-03-28
  • 刊出日期:  2017-11-05

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