外文翻译--并条机自调匀整利用人工神经网络确定在自调匀整作用点（适用于毕业论文外文翻译+中英文对照）.doc

w Textile Research Journal Article Use of Artificial Neural Networks for Determining the Leveling Action Point at the Auto-leveling Draw Frame Assad Farooq1and Chokri Cherif Institute of Textile and Clothing Technology, Technische Universit?t Dresden. Dresden, Germany Abstract Artificial neural networks with their ability of learning from data have been successfully applied in the textile industry. The leveling action point is one of the important auto-leveling parameters of the drawing frame and strongly influences the quality of the manufactured yarn. This paper reports a method of predicting the leveling action point using artificial neural networks. Various leveling action point affecting variables were selected as inputs for training the artificial neural networks with the aim to optimize the auto-leveling by limiting the leveling action point search range. The Levenberg Marquardt algorithm is incorporated into the back-propagation to accelerate the training and Bayesian regularization is applied to improve the generalization of the networks. The results obtained are quite promising. Key words: artificial neural networks, auto-lev-eling, draw frame, leveling action point。 The evenness of the yarn plays an increasingly significant role in the textile industry, while the sliver evenness is one of the critical factors when producing quality yarn. The sliver evenness is also the major criteria for the assessment of the operation of the draw frame. In principle, there are two approaches to reduce the sliver irregularities. One is to study the drafting mechanism and recognize the causes for irregularities, so that means may be found to reduce them. The other more valuable approach is to use auto-levelers [1], since in most cases the doubling is inadequate to correct the variations in sliver. The control of sliver irregularities can lower the dependence on card sliver uniformit