摘要
为有效预测针刺非织造布的孔径及其分布,以针刺密度和针刺深度的不同组合制备了24种聚丙烯针刺非织造布,运用泡点法测取样品的孔径。以针刺密度和针刺深度为输入,建立了基于支持向量机的模型对孔径和孔径变异系数进行预测,并采用交叉验证法进行模型结构参数优化。结果显示,该模型对孔径和孔径变异系数的预测精度均超过98%,且CV值均小于2%。验证实验的结果进一步印证了支持向量机模型具有很高的预测准确度。此外,支持向量机模型的预测性能优于BP神经网络模型。
In order to predict the pore size and its distribution of needle-punched nonwovens effectively, twenty-four kinds of polypropylene needle-punched nonwovens were produced by varying needle density and needle depth. The pore size of these samples was measured via the bubble point method. Taking needle density and needle depth as the inputs, a model based on support vector machine was established to predict the pore size and its variation coefficient of needle-punched nonwovens. And the cross-validation method was used to optimize the structural parameters of the model. Results indicated that the prediction precision for pore size and its variation coefficient were both higher than 98%, and their CV value were both lower than 2%. The result of the subsequent verification experiment further confirmed the high prediction accuracy of the support vector machine model. In addition, the prediction performance of the support vector machine model is better than that of BP neural network model.
出处
《纺织导报》
CAS
2019年第6期104-107,共4页
China Textile Leader
基金
浙江省绍兴市公益性技术应用研究计划项目(2017B70074)
关键词
支持向量机
针刺非织造布
孔径及其分布
预测模型
support vector machine
needle-punched nonwovens
pore size and its distribution
forecasting model
