关键词: 精毛纺, 粗纱工序, BP网络, 相关性分析, 多元逐步回归

Abstract: Based on standardization of the roving working procedure data gathering from worsted mill，in view of the BP neural network input variables effecting the result，the correlation analysis and Multivariate Stepwise Regression Analysis（MSRA） have been proposed respectively to select important parameters that influence the roving CV（R₁） and weight（R₂）.According to the important degree，the chosen parameters were inputted to BP network from large to small in turn；the four BP network models were established with the sub-network way for multi-input single output.The relative Mean Error Percent（MEP） between the forecast value of the 10 groups of testing samples and the observed value are all below 4%.Using the 20 groups of data that do not participate for modeling to forecast the roving quality，the results indicate that：the absolute average precision respectively are 2.63% and 2.98% for R₁ and the R₂ by the correlation analysis；also the correlation coefficients between the forecast and observed value respectively are 0.884 and 0.958；these targets are all better than using MSRA to select parameters for modeling.

Key words: worsted, roving working procedure, BP neural network, correlation analysis, multivariate stepwise regression analysis