中国麻业科学2012，Vol.34Issue(4)：184-189,6.
苎麻纤维性能与成纱质量的人工神经网络分析
Analysis of Ramie Fiber Properties and Yarn Quality by Artificial Neural Network
摘要
Abstract
In this paper, three methods, pure BP neural network, grey relational analysis combined with BP neural network and principal component analysis combined with BP neural network were applied to build models of predicting yarn quality on the basis of ramie fiber properties. The last two methods were expected to reduce the input node numbers of BP neural network, and the network structure could be simplified, therefore the prediction accuracy and stability could be improved. Compared with pure BP neural network, the results gotten from the last two methods were both better, the mean relative error between the predicted results and the measured results of ramie yarn quality, such as the strength, strength irregularity, unevenness and neps, were all reduced greatly.关键词
苎麻/BP神经网络/灰色关联分析/主成分分析Key words
ramie/ BP neural network/ grey relational analysis/ principal component analysis分类
农业科技引用本文复制引用
高晓艳,郁崇文..苎麻纤维性能与成纱质量的人工神经网络分析[J].中国麻业科学,2012,34(4):184-189,6.基金项目
现代农业产业技术体系建设专项资金资助,编号:CARS-19 （）
