人工神经网络技术与纺纱质量预报

评述了人工神经网络和纺纱过程的特点，提出了人工神经网络在纺纱质量预报中的工作原理和网络构建方法，并提供了国内外的应用实例和网络的实现方法，提出人工神经网络技术在纺纱质量预报中的广泛应用前景。     

基于人工神经网络的毛精纺纱线质量预报技术

介绍了毛精纺纺纱过程与人工神经网络的特点 ,提出人工神经网络在纺纱质量预报中的工作原理与实现方法 ,并提供了国内外的应用实例 ,指出人工神经网络技术在毛精纺纱线质量预报中的广泛应用前景。     

纺纱质量预报技术

综述了纺纱质量预报技术的理论与方法研究的现状，着重介绍了基于人工神经网络的纺纱质量预报技术，并举出了应用实例。     

利用神经网络与AFIS纤维测试系统预测纱线质量

评述了人工神经网络以及AFIS纤维测试系统的特点，提出利用人工神经网络和AFIS纤维测试系统进行纺纱质量预测的工作原理和网络构建方法，并提供了实例，说明神经网络可以有效地解决纱线质量预测问题，指出人工神经网络技术在纺纱质量预测和控制中的广泛应用前景。     

纺纱工艺管理与纱线质量预报系统设计

针对纺织企业生产信息化和网络化发展的需求,设计了一种基于B/S模式的纺纱工艺管理与纱线质量预报系统。该系统除了可满足纺织企业日常质量管理的需求外,还特别加强了质量监督功能,并且可利用人工神经网络技术实现对纱线质量的准确预报,提高了生产管理效率和水平,促进了纺纱生产管理的现代化。     

ANN与模糊算法在毛精纺织机效率预报中的对比

 介绍了人工神经网络技术和模糊算法在毛精纺面料织造预报过程中的应用,建立人工神经网络BP质量预报模型和模糊算法中模糊评判模型,利用这2种预报技术分别给出了织机效率预报模型,并比较2种预报模型对毛精纺织造质量预报的性能优劣。通过对人工神经网络技术和模糊算法的理论比较及其预报结果的对比分析,得出在毛精纺织造质量的预报中,人工神经网络BP质量预报模型优于模糊算法中的模糊评判模型,2种预报技术在解决非线性问题方面具有各自的适应性。     

ANN模型与多元线性回归在毛精纺织造预报中的对比

介绍了应用在毛精纺面料织造质量预报过程中的人工神经网络技术(ANN)和多元线性回归方法,给出了2种方法在建立各自模型时的主要工作,并在此基础上建立了织机效率和织疵公分数的2种预报模型,最后通过2种模型的预报结果对比验证了ANN模型和多元线性回归预报模型在毛精纺织造过程预报中的性能,同时得出了2种预报模型在解决线性和非线性问题上的优劣,以及毛精纺织造过程中的纱线品质和工艺参数与织造质量指标之间的线性或非线性关系.     

预报细纱条干不匀率的BP神经网络方法

介绍了BP神经网络及其算法 ,分别建立了两类细纱条干不匀率CV值预报模型 ,并对预报结果分别作了对比分析 ,得出了两类模型的最佳结构 ,从而证明BP神经网络应用于纺纱质量预报的合理性和良好前景。     

BP神经网络模型在纺纱质量预测中的应用

论述了配棉工艺系统中纺纱质量的预测建模与算法实现。通过分析数据仓库的各方面特性，面向纺纱质量设计数据仓库；通过BP算法，对纺纱质量进行网络建模，实现纺纱质量的预测。该系统在某纺织公司运行良好。     

预报细纱条干不匀率的BP神经网络方法

介绍了BP神经网络及其算法，分别建立了两类细纱条干不匀率CV值预报模型，并对预报结果分析作了对比分析，得出了两类模型的最佳结构，从而证明BP神经网络应用于纺纱质量预报的合理性和良好前景。     

利用AFIS与神经网络预测纱线强力

评述了AFIS纤维测试系统以及人工神经网络的特点，提出了利用它们进行纱线强力预测的工作原理和网络构建方法，并提供了实例。     

国毛毛条加工质量预测的人工神经网络模型

简述了毛条加工质量预测的国内外状况，指出了毛条预测加工技术的重要性。根据人工神经网络技术的特点，利用人工神经网络的工作原理描述了建立毛条加工质量预测模型的过程，并给出了毛条质量预测的软件设计思路与模块结构。     

Application of Artificial Neural Networks (ANNs) in Wine Technology

In recent years, neural networks have turned out as a powerful method for numerous practical applications in a wide variety of disciplines. In more practical terms neural networks are one of nonlinear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. In food technology artificial neural networks (ANNs) are useful for food safety and quality analyses, predicting chemical, functional and sensory properties of various food products during processing and distribution. In wine technology, ANNs have been used for classification and for predicting wine process conditions. This review discusses the basic ANNs technology and its possible applications in wine technology.     

Using artificial neural networks with graphical user interface to predict the strength of carded cotton yarns

Artificial neural networks (ANNs) are used in prediction fields. Yarn strength is one of the most important properties, because it reflects the quality of the yarn. The prediction process of yarn strength is very important from the technology side because many of generated forces in the spun yarns could be given by yarn strength. Data were collected from the United Commercial Industrial Company, Damascus, Syria. Then, artificial neural network algorithm was architected. Several neural networks were architected one of these has been chosen, which contained acceptable network error rate. To deal easily with ANN, a simple graphical user interface has been created. This ANN has been tested on a new sample. Results were compared with the actual results as well as the relationship of Solovev which is allocated to predict the strength cotton yarn. ANN has given more acceptable results than Solovev’s relationship.     

运用神经网络与回归分析预测纱线质量

举例说明了利用回归分析和人工神经网络建立纱线质量预测模型的方法。比较了回归分析与神经网络在建立预测模型方法上的优缺点。介绍了如何根据实际需要选择合理的方法建立质量预测模型。     

均匀设计法优化芥菜多糖的提取工艺及其神经网络模型的研究

通过均匀设计实验和二次多项式逐步回归分析得出从芥菜中提取多糖的优化工艺,即提取时间130 min、酶浓度1%、pH4．0、提取温度60℃。在此基础上运用基于均匀设计的人工神经网络构建了工艺参数与芥菜多糖提取率之间的数学模型。在优化的工艺参数下对多糖的提取率进行预测,二次多项式逐步回归的预测值与实测值的相对误差为23．02%,而人工神经网络的预测值与实测值的相对误差仅为4．37%。结果表明,人工神经网络比二次多项式逐步回归分析的预测结果更准确。     

亚麻纤维品质与成纱质量的人工神经网络分析方法

运用人工神经网络的典型模型——“反向传播”模型,建立了由亚麻纤维的品质预报其成纱质量的连接机制模型。最大拟合相对误差不超过3.2%,最大预测相对误差不超过0.23%。实验结果表明,该方法性能良好,在各类纺织品质量分析预测方面有广阔的应用前景。     

Predicting torque of worsted singles yarn using an efficient radial basis function network-based method

The torque in single-spun yarns is an inherent property of the twisting and bending of staple fibres during the formation of yarn combined with the effect of applied tension on the yarn. The consequences of yarn torque are well known and are widely observed as yarn instability, e.g., yarn rotation under tension; local snarling and entanglement at low loads, and as distortion in fabric, i.e., edge-curl and skewing in knitted fabric. In this paper, a method for predicting the yarn torque based on the radial basis function networks is presented and evaluated. This method uses a “universal approximator” based on neural network methodology to minimize noise during training of the network and to approximate the yarn torque as a function of the geometrical and physical parameters of yarns (twist, linear density) and the applied load. The current method is an integral radial basis function network-based approach suitable for textile engineering and gives very good prediction of yarn torque across a range of yarn structural parameters and test conditions.     

A comparative prediction for tensile properties of ternary blended open-end rotor yarns using regression and neural network models

This study focused on predicting tensile properties of PES/CV/PAN blended Open-End Rotor yarns. The effective factors were fiber blend ratios (six stages from 0 to 100%), linear density (three count levels), mixing method (carding machine and drawframe), and number of passages in drawframe (one and two times) as production parameters. We performed a stepwise multiple linear regression (MLR) analysis and established an artificial neural network (ANN) model that trained with backpropagation rule as Levenberg–Marquardt. Then, we conducted a comparative analysis for both models in terms of prediction performance. As a result, ANN has given a slightly better prediction values than MLR for breaking strength but significantly better prediction values for breaking elongation.