人工神经网络控制粗梳毛纺质量的研究

用人工神经网络进行粗梳毛纺毛条质量控制的原理及方法，用基于人工神经网络的计算机自动控制系统改造传统机械式梳毛机，并通过比较基于人工神经网络的计算机自动控制系统与一般自动控制系统，提出了基于人工神经网络的计算机自动控制系统的优越性。     

人工神经网络在食品工业中的应用

人工神经网络（artificial neural network,ANN）是非线性系统,有很强的处理能力,在食品加工中发挥重要的作用。综述讨论人工神经网络技术的原理以及人工神经网络在食品微生物学和食品发酵、感知器、食品干燥、光谱数据分析等食品领域的研究进展。为广大研究者提供人工神经网络技术在该领域应用的研究现状和未来研究趋势,并为人工神经网络在食品工业中更为广泛的应用提供一定理论依据。     

人工神经网络识别丝织物的研究

介绍人工神经网络识别原理、提出人工神经网络识别丝织物的方法，并展示了应用实例。     

人工神经网络在食品工业中的应用

人工神经网络对非线性系统有很强的处理能力，适合对食品加工过程的仿真。综述了人工神经网络在加工：建模、工艺优化、过程控制与预测及在食品分析中的应用，旨在为人工神经网络在食品工业中的更广泛应用提供一定的理论基础和依据。     

人工神经网络在光度法中的应用进展

简述了人工神经网络的主要特点和最常用的类型——误差反向传递网络(BP网络)，将人工神经网络在光度法中的应用分为3个方面：多组分的同时测定、非线性定量、形态分析，并分别讨论了人工神经网络模型算法研究的基本情况。     

对CAD系统设计的ANN系统的发展研究

目前应用广泛的ANN(人工神经网络)大都是应用CAD系统设计的。本文分析了基于CAD系统设计的人工神经网络的结构和特点，并借鉴人脑神经网络的复杂思维，提出人工神经网络的发展方向，并提出未来智能机的模式。     

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。     

ANN在镁合金性能预测中的应用

简述人工神经网络的原理和特点,详细介绍近年来人工神经网络算法在镁合金力学性能、晶粒尺寸、抗拉强度等性能预测中的应用情况,并对进一步提高神经网络预测精度的研究方向进行展望。     

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。     

人工神经网络在食品生物工程中的应用

简述了人工神经网络的基本原理与使用方法,并介绍了其在食品微生物发酵、食品酶工程、食品生物活性物质等食品生物工程领域的研究进展,旨在为人工神经网络在食品工业中的更广泛应用提供一定的理论基础依据。     

人工神经网络在食品工业中的应用

人工神经网络具有非线性的自适应的信息处理能力,已广泛地用于化工、通信等领域。本文综述了神经网络在食品上的应用情况,包括在食品的分类和分级,食品加工过程的仿真和控制,外来物与掺假物的鉴别,气味分析以及感观评定等方面的应用。在此基础上,对神经网络在食品工业上的应用前景进行了探讨。     

ANN在发酵过程中的应用

人工神经网络具有非线性的自适应的信息处理能力 ,已广泛地用于化工、通信等领域。文中综述了ANN在发酵过程的仿真与预测、错误诊断、自动控制和配方优化等方面的应用。在此基础上 ,对ANN在发酵工业中的应用前景进行了展望。     

Application of data mining technique in predicting worsted spun yarn quality

Today’s industry gives first priority to information technology. Since understanding the structures and relationships dominated of data can help industrial managers to attend in competitive market successfully, a special mechanism must be developed to process data stored in a system. Hence, the focus on widespread use of data mining gains increasing attention. The purpose of this paper is using data-mining technique in textile industry. More than 150,000 data includes testing of raw materials, manufacturing process parameters and yarn quality parameters, during one year in worsted spinning factory were collected. Next, yarn quality was predicted by using data-mining methods containing clustering and artificial neural network (ANN). In order to evaluate the proposed method, the results obtained were compared with conventional methods based on ANN. The results showed that the performance of data-mining technique is more accurate than that of ANN.     

纺织工业中的虚拟加工技术与模式

简要介绍了纺织品加工过程、人工神经网络（ANN）及其相关算法的特征。通过ANN技术建立的原料、纺纱、织造和后整理预测／反演模型，能够优化生产工艺，预测与控制产品质量，是纺织设计与虚拟加工的基础。采用主因子、聚类、案例模板和ANN等算法完成对输入参数的归纳、筛选与增补，是提高预测／反演模型精度和效率的有效步骤。以此构建的模块组合式虚拟加工系统，对纺织工业的快速、准确和理性加工，纺织品的低成本和高质量实现，具有重要意义。     

A critical review on the use of artificial neural networks in olive oil production,characterization and authentication

Artificial neural networks (ANN) are computationally based mathematical tools inspired by the fundamental cell of the nervous system, the neuron. ANN constitute a simplified artificial replica of the human brain consisting of parallel processing neural elements similar to neurons in living beings. ANN is able to store large amounts of experimental information to be used for generalization with the aid of an appropriate prediction model. ANN has proved useful for a variety of biological, medical, economic and meteorological purposes, and in agro-food science and technology. The olive oil industry has a substantial weight in Mediterranean's economy. The different steps of the olive oil production process, which include olive tree and fruit care, fruit harvest, mechanical and chemical processing, and oil packaging have been examined in depth with a view to their optimization, and so have the authenticity, sensory properties and other quality-related properties of olive oil. This paper reviews existing literature on the use of bioinformatics predictive methods based on ANN in connection with the production, processing and characterization of olive oil. It examines the state of the art in bioinformatics tools for optimizing or predicting its quality with a view to identifying potential deficiencies or aspects for improvement.     

Determination of cleaning end of dairy protein fouling using an online system combining ultrasonic and classification methods

Fouling and cleaning of heat exchangers in food industry are severe and costly issues and of high importance. In this study, a planar heat exchanger was constructed to produce and clean milk protein fouling similar to industry. Using a combination of an ultrasonic measuring method and classification machines cleaning should be monitored online to adapt cleaning time. After reproducible fouling deposit was built, cleaning started which was monitored using an ultrasonic measuring unit. The measured ultrasonic signal was analyzed for seven acoustic features and fed together with temperature and mass flow rate (both measured) into a classification method for decision of fouling presence or absence. For classification, artificial neural network (ANN) and support vector machine (SVM) was applied displaying detection accuracies of more than 80 % (ANN) and 94 % (SVM), respectively. Besides, the slope change of the seven acoustic features was monitored with time resulting in a cleaning time of at least 21?±?4 min. The cleaning time determined by the new sensor system is comparable with previously determined cleaning times for this setup. This study demonstrated that ultrasound based sensor systems offer a new tool to determine presence or absence of fouling and to monitor cleaning processes in the food industry with high accuracy.     

MODELING DEAD-END ULTRAFILTRATION OF APPLE JUICE USING ARTIFICIAL NEURAL NETWORK

An artificial neural network (ANN) was developed to model the dead-end ultrafiltration process of apple juice. Molecular weight cutoff, transmembrane pressure, gelatin–bentonite concentration and time were the input variables, while filtrate flux and filtrate volume were the output variables of the ultrafiltration process. According to error results and correlation values for two types of network (one or two hidden layer configurations), configurations with two hidden layers had comparatively better performance. The highest correlation coefficient with the minimum prediction error was calculated for two hidden layers with 6-5 nodes configuration. Trained ANN (4-6-5-2) predicted filtrate flux and filtrate volume with 2.33 and 1.38% mean relative error, respectively. The results suggest that the ANN modeling can be effectively used to optimize filtration process.

PRACTICAL APPLICATION

Membrane separation processes including ultrafiltration have gained importance in the food industry. Today, fruit juices are widely clarified by means of ultrafiltration process instead of tedious and laborious conventional clarification treatments. Membrane fouling which results in flux decline is the main problem associated with the ultrafiltration of fruit juices. In order to perform an efficient ultrafiltration process, optimization is required to obtain maximum filtrate volume per unit time. Artificial neural network (ANN) modeling offers great advantage on improving the performance of ultrafiltration process by accounting the effects of different variables, i.e., feed properties, transmembrane pressure and membrane pore size on filtrate volume as the main output of the filtration process. ANN modeling of ultrafiltration may be an alternative to previously proposed empirical and semiempirical models.     

Calibration of artificial neural network and partial least squares regression models for the prediction of secoisolariciresinol diglucoside contents in microwave-assisted extracts of various flaxseed (Linum usitatissimum L.) samples

Partial least squares (PLS) and artificial neural network (ANN) regression models were calibrated for predicting the content of secoisolariciresinol diglucoside (SDG) in six flaxseed cultivars, defatted flaxseed meal and flax hulls extracts. The SDG was quantified by HPLC after microwave-assisted extraction (MAE) from flaxseed; the data were used in conjunction with the light absorption of the extracts measured after Folin–Ciocalteu’s assay at 289, 298, 343 and 765 nm, in order to calibrate the predictive PLS and ANN models. The accuracy and the predictive ability of the models ranged from good to excellent as indicated by RPD values (the ratio of the standard deviation of the reference values to the standard error of prediction) of 5.03–13.7. The PLS and ANN predictive models are useful to the flaxseed processing industry for rapidly and accurately predicting the SDG contents of various flaxseed samples based on their UV–Vis light absorption.