| 基于最小方差支持向量机的织物热湿舒适性预测 |
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| 引用本文： | 辛芳芳. 基于最小方差支持向量机的织物热湿舒适性预测[J]. 纺织学报, 2011, 32(7): 60-64 |
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| 作者姓名： | 辛芳芳 |
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| 作者单位： | 上海工程技术大学服装学院,上海,201620 |
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| 摘 要： | 在纺织服装工程研究中应用人工智能与机器学习的方法,可以更加准确地预测纺织材料的穿着热湿舒适性.为此,利用最小方差支持向量机( LSSVM),分析了36种针织织物热湿舒适性客观指标与人体穿着对织物的热湿舒适性主观评定之间的对应关系,并建立了客观指标与主观评定之间的回归模型.该模型能够快速预测成衣之后人体穿着主观评定的舒适...
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| 关 键 词： | 针织织物 人工智能 热湿舒适性 回归分析 核方法 最小方差支持向量机 机器学习 |
Prediction of fabric thermal-moisture comfort based on least squares support vector machines |
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| XIN Fangfang. Prediction of fabric thermal-moisture comfort based on least squares support vector machines[J]. Journal of Textile Research, 2011, 32(7): 60-64 |
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| Authors: | XIN Fangfang |
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| Affiliation: | XIN Fangfang(Fashion College of Technology,Shanghai University of Engineering Science,Shanghai 201620,China) |
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| Abstract: | The application of artificial intelligence and machine learning methods to textile & fashion engineering facilitates the prediction of thermal-moisture comfort of fabrics.Thirty-six kinds of knitted fabrics are investigated and the relationship between the thermal-moisture comfort objective evaluation indices and the subjective wear evaluation indices of the fabrics are analyzed by least squares support vector machines(LSSVM).And regression models are created to predict the subjective evaluation using objec... |
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| Keywords: | knitted fabric artificial intelligence thermal-moisture comfort regression analysis Kernel methods least squares support vector machines machine learning |
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