Prediction of Hot Ductility of Low-Carbon Steels Based on BP Network

The purpose of the research is to obtain an effective method to predict the hot ductility of low-carbon steels, which will be a reference to evaluate the crack sensitivity of steels. Several sub-networks modeled from BP network were constructed for different temperature use, and the measured reduction of area (AR) of 12 kinds of low-carbon steels under the temperature of 600 to 1000 ℃ were processed as training samples. The result of software simulation shows that the model established is relatively effective for predicting the hot ductility of steels.

The Application of BP Networks to Land Suitability Evaluation

The back propagation (BP) model of artificial neural networks (ANN) has many good qualities comparing with ordinary methods in land suitability evaluation.Through analyzing ordinary methods’ limitations,some sticking points of BP model used in land evaluation,such as network structure,learning algorithm,etc.,are discussed in detail,The land evaluation of Qionghai city is used as a case study.Fuzzy comprehensive assessment method was also employed in this evaluation for validating and comparing.

Real-time multi-step prediction control for BP network with delay

Real time multi step prediction of BP network based on dynamical compensation of system characteristics is suggested by introducing the first and second derivatives of the system and network outputs into the network input layer, and real time multi step prediction control is proposed for the BP network with delay on the basis of the results of real time multi step prediction, to achieve the simulation of real time fuzzy control of the delayed time system.

MENDED GENETIC BP NETWORK AND APPLICATION TO ROLLING FORCE PREDICTION OF 4-STAND TANDEM COLD STRIP MILL

In order to make good use of the ability to approach any function of BP (back propagation) network and overcome its local astringency,and also make good use of the overall search ability of GA (genetic algorithms),a proposal to regulate the network's weights using both GA and BP algorithms is suggested.An integrated network system of MGA (mended genetic algorithms) and BP algorithms has been established.The MGA-BP network's functions consist of optimizing GA performance parameters,the network's structural parameters,performance parameters,and regulating the network's weights using both GA and BP algorithms.Rolling forces of 4-stand tandem cold strip mill are predicted by the MGA-BP network,and good results are obtained.

Classification of Infrared Monitor Images of Coal Using an Feature Texture Statistics and Improved BP Network

It is very important to accurately recognize and locate pulverized and block coal seen in a coal mine's infrared image monitoring system. Infrared monitor images of pulverized and block coal were sampled in the roadway of a coal mine. Texture statistics from the grey level dependence matrix were selected as the criterion for classification. The distributions of the texture statistics were calculated and analysed. A normalizing function was added to the front end of the BP network with one hidden layer. An additional classification layer is joined behind the linear layer. The recognition of pulverized from block coal images was tested using the improved BP network. The results of the experiment show that texture variables from the grey level dependence matrix can act as recognizable features of the image. The innovative improved BP network can then recognize the pulverized and block coal images.

Performance of Feedback BP Networks

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Optimization of Injection Molding Process of Bearing Stand Based on BP Network Method

The quality of injection plastic molded parts relates to precise geometry,smooth surface,strength,durability,and other indicators that are associated with the mold,materials,injection process,and service environment.The warpage is one of main defects of injection products,which cost much time and materials.In order to minimize warpage to ensure the precise shape of molded parts,it needs to combine design,service conditions,process parameters,material properties,and other factors in the design and manufacturing.Finite element tools and material database are used to analyze the occurrence of warpage,and analysis results contribute to the improvement and optimization of injection molding process of typical parts.To find the optimal process parameters in the solution space,experimental data are used to establish backpropagation(BP)network for predicting warpage of a bearing stand based on analysis with Moldflow.With a proper transfer function and the BP network architecture,results from the BP network method satisfiy the criteria of accuracy.The optimal solutions are searched in the BP network by the genetic algorithm with the finding that the optimization method based on the BP network is efficient.

Forecasting Loss of Ecosystem Service Value Using a BP Network: A Case Study of the Impact of the South-to-north Water Transfer Project on the Ecological Environmental in Xiangfan, Hubei Province, China

Objective To recognize and assess the impact of the South-to-north Water Transfer Project(SNWTP) on the ecological environment of Xiangfan, Hubei Province, situated in the water-out area,and develop sound scientific countermeasures. Methods A three-layer BP network was built to simulate topology and process of the eco-economy system of Xiangfan. Historical data of ecological environmental factors and socio-economic factors as inputs, and corresponding historical data of ecosystem service value (ESV) and GDP as target outputs, were presented to train and test the network. When predicted input data after 2001 were presented to trained network as generalization sets, ESVs and GDPs of 2002, 2003, 2004… till 2050 were simulated as output in succession.Results Up to 2050, the area would have suffered an accumulative total ESV loss of RMB104.9 billion, which accounted for 37.36% of the present ESV. The coinstantaneous GDP would changea synchronously with ESV, it would go through an up-to-down process and finally lose RMB89.3 billion, which accounted for 18.71% of 2001. Conclusions The simulation indicates that ESV loss means damage to the capability of socio-economic sustainable development, and suggests that artificial neural networks (ANNs) provide a feasible and effective method and have an important potential in ESV modeling.

Fast Quantification of the Mixture of Polycyclic Aromatic Hydrocarbons Using Surface-Enhanced Raman Spectroscopy Combined with PLS-GA-BP Network

To realize the fast and accurate quantitative analysis of the mixture of polycyclic aromatic hydrocarbons(PAHs),surface-enhanced Raman spectroscopy(SERS)coupled with multivariate calibrations were employed.In this study,three kinds of calibration algorithms were used to quantitative analysis of the mixture of naphthalene(Nap),phenanthrene(Phe),and pyrene(Pyr).Firstly,partial least squares(PLS)algorithm was used to select characteristic variables,then the global search capability of genetic algorithm(GA)was used for the determining of the initial weights and thresholds of back propagation(BP)neural network so that local minima was avoided.The PLS-GA-BP model exhibited superiority to quantify PAHs mixture,which achieved R2=0.9975,0.9710,0.9643,ARE=10.07%,19.28%,16.72%and RMSE=13.10,5.40,5.10 nmol L−1 for Nap,Phe,Pyr(in the PAHs mixture)concentration prediction respectively.The forecast error,ARE and RMSE have been reduced more than 50%and 60%respectively compared with the whole spectral BP model.The study indicates that accurate quantitative spectroscopic analysis of the mixture of PAHs samples can be achieved through the combination of SERS technique and PLS-GA-BP algorithm.

Application of genetic BP network to discriminating earthquakes and explosions

In this paper, we develop GA-BP algorithm by combining genetic algorithm (GA) with back propagation (BP) algorithm and establish genetic BP neural network. We also applied BP neural network based on BP algorithm and genetic BP neural network based on GA-BP algorithm to discriminate earthquakes and explosions. The obtained result shows that the discriminating performance of genetic BP network is slightly better than that of BP network.

BP Network Based Users’Interest Model in Mining WWW Cache

By analyzing the WWW Cache model, we bring forward a user-interest description method based on the fuzzy theory and user-interest inferential relations based on BP(back propagation) neural network. By this method, the users’ interest in the WWW cache can be described and the neural network of users’ interest can be constructed by positive spread of interest and the negative spread of errors. This neural network can infer the users’ interest. This model is not the simple extension of the simple interest model, but the round improvement of the model and its related algorithm.

Design of an Inference Engine Based on BP Network and LabVIEW

In the field of engineering, LabVIEW and MATLAB are the languages most commonly used by program developers. Howev-er, they have their respective advantages and disadvantages. The combination of the two will undoubtedly facilitate program develop-ment. Design of inference engine is the key point and difficulty in the design of fault diagnosis expert system. This paper combinesLabVIEW and MATLAB to design the inference engine of fault diagnosis expert system based on the advantages of the graphical pro-gramming environment and signal analysis toolkit of LabVIEW without the defect of neural network toolkit. In addition, it introducesthe implementation methods and precautions for combination of LabVIEW and BP network so as to make LabVIWE amd BP benefitfrom each other, which is of great pragmatic value.

iRoot BP Plus冠髓切断术治疗乳磨牙部分不可复性牙髓炎的回顾性研究

目的 回顾性观察iRoot BP Plus冠髓切断术治疗乳磨牙部分不可复性牙髓炎的临床疗效。方法 收集2019年1月—2023年8月行乳磨牙iRoot BP Plus冠髓切断术治疗且随访24~47个月的部分不可复性牙髓炎病例102例,根据术前有无不可复性牙髓炎症状将纳入病例分为无症状组(n=53)和有症状组(n=49),观察两组的临床和影像学成功率。结果 无症状组和有症状组的临床成功率分别为96.2%和97.9%,影像学成功率分别为96.2%和93.9%。结论 在高抗菌等级前提下,iRoot BP Plus冠髓切断术可以尝试用于治疗乳磨牙部分不可复性冠髓炎。

正交实验结合AHP和GA-BP神经网络优化益黄散醇提工艺

目的 优化益黄散的醇提工艺。方法 采用回流提取法,以乙醇体积分数、液料比、提取时间为考察因素设计正交实验,以橙皮苷、川陈皮素、橘皮素、没食子酸、诃黎勒酸、诃子酸、甘草苷、甘草酸、丁香酚含量和干浸膏得率为指标,采用层次分析法(AHP)进行赋权并计算综合评分。通过验证正交实验和遗传算法(GA)-反向传播神经网络(BP神经网络)所预测的结果确定益黄散最佳醇提工艺参数。结果 正交实验优选的最佳醇提工艺参数为乙醇体积分数60%、液料比14∶1(mL/g)、提取时间90 min、提取2次,验证所得综合评分为79.19分;GA-BP神经网络优选的最佳醇提工艺参数为乙醇体积分数65%、液料比14∶1(mL/g)、提取时间60 min、提取2次,验证所得综合评分为85.30分,高于正交实验所得结果。结论 采用正交实验结合GA-BP神经网络的寻优方法较传统的正交实验寻优方法效果更佳,其优选出的益黄散最佳醇提工艺稳定可靠。

基于BP-DEMATEL的山西省冬小麦水足迹影响因素识别

针对山西省冬小麦水足迹历史演变及关键影响因素识别问题,分析了1993—2021年山西省冬小麦水足迹历史演变规律,利用BP神经网络-决策实验室(BP-DEMATEL)模型对冬小麦水足迹演变的关键影响因素进行识别,将其分为驱动型因素及特征型因素,并揭示了影响因素之间的作用机制。结果表明:1993—2021年山西省冬小麦水足迹及单位水足迹均呈下降趋势;2021年山西省冬小麦总水足迹为27亿m^(3)(蓝水占比57%),单位水足迹为1 122 m^(3)/t,与前期高点相比分别下降38%(较1994年)和21%(较1993年);气温、相对湿度和灌溉面积为冬小麦水足迹演变的关键驱动型因素,化肥施用量和农业机械总动力为关键特征型因素。

基于MPSO-BP算法的四电极电化学气体传感器温度补偿研究

针对四电极电化学气体传感器的测量精度极易受环境温度影响的问题,提出一种基于粒子群优化BP神经网络算法(PSO-BP)的温度补偿方法。利用改进的PSO算法(MPSO)对BP神经网络的权值和阈值进行优化,构造四电极电化学气体传感器的温度补偿模型,并设计了气体传感器测试系统。实验结果表明,MPSO-BP算法可有效提高BP神经网络的收敛速度和泛化能力;基于MPSO-BP算法的四电极气体传感器温度补偿模型,可将温度补偿误差控制在0.1%以内。

基于LDA-BP神经网络的高校思政课教师数据驱动决策力评价研究

数据驱动决策力为高校思政课教师提供科学合理的教学判断,对数据驱动决策力进行评价研究,有助于提高高校思政课教师的数据决策水平,进而提升思想政治教育教学质量。鉴于传统评测方法缺乏客观性与可重复性,运用LDA-BP神经网络技术构建高校思政课数据驱动决策力的指标体系与评价模型。首先,运用LDA方法对高校思想政治教育相关的政策文本与研究文献进行主题提取,并将主题信息作为指标构建基础;其次,通过研读文献与政策文本,并结合主题分析结果构建高校思政课教师数据驱动决策力评价指标体系;最后,通过对BP神经网络的训练及测试来生成高校思政课教师数据驱动决策力的评价模型。研究表明,高校思政课教师的专业知识、教学水平以及数据分析与解读能力是影响数据驱动决策能力的关键因素,据此,理应从素养提升、文化培育、管理革新、政府支持等方面入手增强数据驱动决策力。

基于FA-BP神经网络的生姜干燥含水率预测

为探索生姜的干燥特性,并实现生姜干燥的含水率预测,研究了不同干燥温度(50、55、60℃)、干燥风速(1.0、2.0、3.0m/s)、切片长度(30、35、40mm)对生姜干燥时间和干燥速率的影响。结合BP神经网络自适应能力、泛化能力、学习能力强和萤火虫算法(FA)参数少、寻优能力强、收敛速度快等特点,将干燥温度、干燥风速、切片长度和干燥时间作为输入层,隐藏层个数为10,输出层为生姜的含水率,搭建一个拓扑结构为“4-10-1”的FA-BP神经网络模型。研究结果表明:干燥温度、干燥风速、切片长度都是影响生姜含水率的关键因素,增加干燥风速、提高干燥温度和减少切片长度能有效缩短生姜的干燥时间,提高干燥效率。选用萤火虫算法优化BP神经网络的权值和阈值,减少了神经网络的训练时间,提高了精准度,其含水率预测值与试验值之间的决定系数R2=0.999 02,均方根误差RMSE为0.002 99,含水率预测结果准确且迅速,能够为生姜干燥过程中的含水率在线预测提供科学依据。