基于神经网络的精梳毛纺纱线预测

在分析纺纱原理的基础上，采用人工神经网络建立7个不同的模型，预测精梳毛纺的纱线质量和纺纱性能，分别是纱线不匀、粗细节、断裂强力、强力不匀、断裂伸长和断头率。其中断头率由于其复杂的成因，采用了组合神经网络建模。采用工厂实际生产数据进行验证，前6个指标的预测值与实测值之间的相关系数的平方均超过0．9，断头的预测效果相比而言比较差，但相关系数的平方也超过了0．8，表明人工神经网络技术在精梳毛纺纱线预测方面有很大的应用前景。     

基于PSO-BP神经网络的纱线质量预测

针对复杂纺纱过程中成纱断裂强度难以预测的问题,提出一种基于粒子群优化算法(PSO)优化BP神经网络的成纱断裂强度预测方法.该方法采用PSO优化神经网络的权值和阈值,用来提高神经网络的收敛速度和获得全局最优解的能力.以纺纱车间大量现场质量检测数据为对象,进行预测验证,结果表明,PSO-BP神经网络在预测相关性(预测值与实际值的一致性程度)上与传统BP算法相比提高5.0%,与GA-BP算法相比提高4.6%,在预测精度上均要好于BP神经网络与GABP神经网络.     

低捻转杯纺纱张力与断头分析

讨论了转杯纺纱过程中捻度分布、张力分布以及断头率的测定分析.通过测定、分析与讨论,得出低捻转杯纺纱过程中,断头率急剧增加,接头成功率大大减少,同时断头的部位大部分发生在纺纱转杯至引纱管区域.因此增加转杯至引纱管区域的捻度,提高该纱段的成纱强力是低捻转杯纺纱的主要关键.     

智能预报模式与水文中长期智能预报方法

建立了以模糊优选、BP神经网络及遗传算法有机结合的智能预报模式与方法。在应用该方法进行中长期水文智能预报时，首先选取训练样本的数量，根据预报因子与预报对象的相关关系得到相对隶属度矩阵;再将其作为BP神经网络输入值以训练连接权重；最后将得到的连接权重值用于预报检验。计算结果表明，智能预报模式与方法的运行速度、精度及稳定性都达到了实际应用的要求。     

二十一世纪纺纱工程展望

二十一世纪，一些相关高新技术的发展和应用，将对传统工程产生巨大的影响和推动。文章在此基础上，对新世纪前半叶纺纱技术、纺纱设备以及纺纱工厂自动化的发展进行了展望。     

基于BP神经网络的切削力预报

切削力预报与控制直接影响切削加工的质量和成本.以多层前馈神经网络为基本结构,以误差反向传播算法(BP算法)为网络训练方法,借助VC 语言建立了切削力预报程序.通过引入共轭梯度法和拟牛顿法优化方法,解决了网络训练中局部最小和过早饱和问题,提高了神经网络的收敛速度和精度,实现了对切削加工过程中切削力的预报和仿真.通过对以两种难加工材料的铣削和磨削试验数据为基础的预报计算,发现传统经验公式方法预报误差偏大,最大相对误差达24.9%,而神经网络方法预报结果最大相对误差为2.01%,证明基于BP神经网络的切削力预报研究具有一定参考和应用价值.     

紧密纺纱与超大牵伸纺纱技术分析

对紧密纺和超大牵伸纺这两种纺纱技术进行了分析，从工艺设备和理论上探讨了紧密纺纱和超大牵伸纺纱的成纱质量高于普通环锭纺纱的原因。     

人工神经网络在苏州空气污染预报中的应用

人工神经网络在预测预报领域的应用越来越广泛。简单介绍了BP神经网络的基本原理,较详细地回顾了国内BP神经网络在空气污染预报领域的研究应用情况,并建立了苏州市区SO2浓度预报的BP神经网络,预报结果表明:该模型具有简便、快速、准确的优点,可推广用于其它空气污染物的预报。     

微型气圈环──降低和稳定纺纱张力的有效措施

环锭纺纱机可通过使用轻的钢丝圈来降低纺纱张力和断头率。但钢丝圈太轻会导致气圈膨大，并碰撞隔纱板，因此张力的降低极其有限。理论和实验证明，直径远小于管纱直径的微型气圈环能更有效地降低和稳定纺纱张力。对于细度为１４．２～２５．０ｔｅｘ的纱，张力可降低１０％；如与轻的钢丝圈搭配使用，在锭速为７０００～１３０００ｒ／ｍｉｎ时纺１４．２和２５．０ｔｅｘ纱，张力可降低２４～２９％，而且更为稳定。     

基于GA优化BP神经网络辨识的Volterra级数核估计算法

为取得更有效的船舶运动预报效果,提出了一种利用遗传算法(GA)优化单输出三层反向传播(BP)神经网络辨识Volterra级数核的算法.在船舶航行姿态时间序列的混沌特性识别基础上,分析了GA、BP神经网络和Volterra级数模型的特征.利用GA优化BP神经网络获得最优的初始权值和阈值,根据BP神经网络算法求得最终的最优权值和阈值.进行Taylor级数分解,得到Volterra级数各阶核,对船舶的横摇运动时间序列进行多步预报.仿真实验表明:所提方法预报精度高、时间长,具有有效性和适应性.     

A Worsted Yarn Virtual Production System Based on BP Neural Network

Back-Propagation (BP) neural network and its modified algorlthm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qua/ides. The results from these two series of models have been compared with the measured values respectively, proving that the accuracy in both the prediction model and the deduction model is high. The experimental results and the corresponding analysis show that the BP neural network is an efficient technique for the quality prediction and has wide prospect in the application of worsted yam production system.     

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters（ i. e.,raw material index inputs） have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network（ KBANN） was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set（ RS）-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.     

Applications of Neural Networks in Spinning Prediction

Th netal network spinning prediction model (BPana RBF Networks) trained by data from the mill canpredict yarn qualities and spinning performance. Theinput parameters of the model are as follows: yarncount, diameter, hauteur, bundle strength, spinningdraft, spinning speed, traveler number and twist.     

Yarn Properties Prediction Based on Machine Learning Method

Although many works have been done to constructprediction models on yarn processing quality, the relationbetween spinning variables and yam properties has not beenestablished conclusively so far. Support vector machines(SVMs), based on statistical learning theory, are gainingapplications in the areas of machine learning and patternrecognition because of the high accuracy and goodgeneralization capability. This study briefly introduces theSVM regression algorithms, and presents the SVM basedsystem architecture for predicting yam properties. Model.selection which amounts to search in hyper-parameter spaceis performed for study of suitable parameters with grid-research method. Experimental results have been comparedwith those of artificial neural network(ANN) models. Theinvestigation indicates that in the small data sets and real-life production, SVM models are capable of remaining thestability of predictive accuracy, and more suitable for noisyand dynamic spinning process.     

基于BP人工神经网络的大气颗粒物PM10质量浓度预测

根据2008年长沙市火车站监测点全年大气PM10及气象参数的小时平均数据,建立BP人工神经网络预测模型,预测PM10小时平均浓度。为证明人工神经网络模型用于预测PM10质量浓度的准确性,研究中考虑2种预测模型:多元线性回归模型与人工神经网络模型。研究结果表明:与传统的多元线性回归模型相比,人工神经网络模型能够捕捉污染物浓度与气象因素间的非线性影响规律,能更好地预测PM10质量浓度,拟合优度R2有较大提高;所选取气象参数及污染源强变量能较准确地描述大气PM10质量浓度的实时变化,用于PM10质量浓度的预测准确度较高,整体R2可达0.62;人工神经网络预测模型不仅适用于一般污染浓度情况,对于高污染时期PM10质量浓度的预测也较为准确。     

REPORT ON A PROJECT AIMED AT INCREASING THE STRENGTH OF FRICTION SPUN YARN BY USING A FALSE TWIST PROCESS

The lower strength of friction spun yarns has became a key problem that hinder the develop-ment of friction spinning,especially in high speeds and fine yarns.In this paper a project aimed atincreasing the strength of friction spun yarns by using a false twist process has been made.In re-ported experiment false twist was inserted into the yarn which has been formed on a friction spin-ning machine,so that it made the yarn untwist at first,then regain the twist.In the meantime draftwas applied to generate more tension than that which had been experienced during yarn formationon the friction spinning machine.It made the yarn reforming and the loosely bound fibres in thefriction spun yarn formed due to extremely low tension on the friction spinning machine becametighter.Results indicate that with the proper draft,tension and false twist the tenacity of processedyarn will increase more than 10%.This project is an attempt to discover whether improvements can be made and whether it isworth pursuing research into modifications of yarn structure.The results imply that if the yarntension can be raised during yarn formation on the friction spinning machine or a chance is offeredto the yarn to reform after the yarn formation the yarn tenacity will be increased obviously.     

紬丝包芯纺纱的探索

本文介绍了一般的包芯纱,包绕纱中纤维的组合形式、纺纱方法及成纱特点。在此基础上介绍了(纟由)丝包芯纺纱的工艺、试验所用的材料、成纱品质及其与(纟由)丝的比较。从而指出(纟由)丝包芯纱兼备了(纟由)丝和化纤长丝的特点。因此,(纟由)丝包芯纺纱有继续探索的必要和可能。     

AN INVESTIGATION OF THE RELATION BETWEEN COTTON FIBRE PROPERTIES AND YARN QUALITY BY USING HIGH VOLUME INSTRUMENT(HVI)SYSTEM

The objective of this paper is to establish reliable prediction equations relating cotton fibreproperties measured by HVI system and yarn quality,A useful statistical method is adopted for de-veloping a multiple regression model interpreting the relation between the data of Spinlab HVIfibre properties and quality parameters of yarn STQ (Strength Tex Quotient).The percent relativecontribution of a fibre property with respect of STQ is also assessed.The results show that the totalcontribution of the HVI measured fibre properties can account for 77.4% of known variation ofyarn STQ.The main feature of the approach is its flexibility in accommodating all fibre properties.The examination of regression equation showed that it could be well applied to predict STQ ofyarns spun from the same spinning system,but,for different spinning systems and also for yarns ofdifferent linear density,modification of the equation would be needed.     

苎麻长纤维与棉纤维混纺的研究

由于牵伸装置的局限性,一般长纤维和短纤维纺纱是分别在不同设备上进行的。苎麻/棉混纺纱也都是以苎麻短纤维(落麻或切段麻)和棉纤维按短纤维纺纱系统混纺而成的。由于原料性能的限制,其成纱质量较低,尤其是条干不匀、麻粒等方面较差,难以满足高档织物的要求。本文通过对长、短纤维在长纤维纺纱设备上进行各种方案的纺纱试验,证明了只要合理调整牵伸装置等工艺条件,长短纤维在同一设备上纺纱是可行的。并对纺出的长麻/棉混纺纱的性能作了测试分析。结果表明,由于纺纱原料性能的改善,长麻/棉混纺纱的质量较短麻/棉混纺纱的质量有了较大提高,这对开拓麻/棉混纺纱生产和市场有一定的意义。