| Prediction of Anthropometric Dimensions Based on Grey Incidence Analysis and ANFIS |
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| 作者姓名： | 丛杉 崔志英 张渭源 |
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| 作者单位： | [1]Fashion College ,Shanghai University of Engineering Science, Shanghai 201620, China [2]Fashion Institute, Donghua University .Shanghai 200051, China |
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| 基金项目： | Foundation item： Shanghai Board of Education Scientific Research Projects （No. 106N2013） |
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| 摘 要： | In order to select the efficient input variables of adaptive ncuro-fuzzy infence system （ANFIS）during the prediction anthropometric dimenions, grey incidence （GI） analysis, as a mastic method that ranks the sequence of of lots of variables in complicated factors has been applled.According to the prediction accuracy （A） between the predicted values and actual measured values, the ANFISG1 model with the parameters selected by using the GI analysis were more correct and effective than those done by multiple regression model and the model with input parmeters nonelected. The model prediction accuracy △Regrauskn= 0.804 7〈 △ANE3CI=0.9725, which proves the nodel with few parameters is more correct and effective than the other merits.
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| 关 键 词： | 缝纫设备 纺织工业 纺织技术 服装加工设备 |
| 文章编号： | 1672-5220（2007）03-0386-05 |
| 修稿时间： | 2006-08-20 |
Prediction of Anthropometric Dimensions Based on Grey Incidence Analysis and ANFIS |
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| CONG Shan,CUI Zhi-ying,ZHANG Wei-yuan.Prediction of Anthropometric Dimensions Based on Grey Incidence Analysis and ANFIS[J].Journal of Donghua University,2007,24(3):386-390. |
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| Authors: | CONG Shan CUI Zhi-ying ZHANG Wei-yuan |
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| Institution: | 1. Fashion College, Shanghai University of Engineering Science, Shanghai 201620, China;Fashion Institute, Donghua University, Shanghai 200051 ,China
2. Fashion Institute, Donghua University, Shanghai 200051 ,China |
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| Abstract: | In order to select the efficient input variables of adaptive neuro-fuzzy inference system (ANFIS)during the prediction anthropometric dimensions, grey incidence (GI) analysis, as a mathematic method that ranks the sequence of importance of lots of variables in complicated factors has been applied. According to the prediction accuracy (A) between the predicted values and actual measured values, the ANFISGI model with the parameters selected by using the GI analysis were mote correct and effective than those done by multiple regression model and the medel with input parameters naneleoted. The model prediction accuracy △Regression = 0.804 7< △ ANFISGI =0.9725, which proves the model with few parameters is mate correct and effective than the other methods. |
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| Keywords: | anthropometric dimensions GI ANFIS prediction approach |
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