Monthly Mean Temperature Prediction Based on a Multi－level Mapping Model of Neural Network BP Type

In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level model of this type with ixj=3x2,k=l,and the 1980 monthly mean temperture predichon on a long-t6rm basis were prepared by steadily modifying the weighting coefficient,making for the correlation coefficient of 97% with the measurements.Furthermore,the weighhng parameter was modified for each month of 1980 by means of observations,therefore constrcuhng monthly mean temperature forecasts from January to December of the year,reaching the correlation of 99.9% with the measurements.Likewise,the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlahon of 98% and the month-tO month forecasts of 99.4%.

HCl emission characteristics and BP neural networks prediction in MSW/coal co-fired fluidized beds

The HCl emission characteristics of typical municipal solid waste(MSW) components and their mixtures have been investigated in a Φ150 mm fluidized bed. Some influencing factors of HCl emission in MSW fluidized bed incinerator was found in this study. The HCl emission is increasing with the growth of bed temperature, while it is decreasing with the increment of oxygen concentration at furnace exit. When the weight percentage of auxiliary coal is increased, the conversion rate of Cl to HCl is increasing. The HCl emission is decreased, if the sorbent(CaO) is added during the incineration process. Based on these experimental results, a 14×6×1 three-layer BP neural networks prediction model of HCl emission in MSW/coal co-fired fluidized bed incinerator was built. The numbers of input nodes and hidden nodes were fixed on by canonical correlation analysis technique and dynamic construction method respectively. The prediction results of this model gave good agreement with the experimental results, which indicates that the model has relatively high accuracy and good generalization ability. It was found that BP neural network is an effectual method used to predict the HCl emission of MSW/coal co-fired fluidized bed incinerator.

Ecological Carrying Capacity Prediction of Huainan City Based on GM–BP Neural Network

Evaluation of ecological carrying capacity is an important method of analyzing regional sustainable development, study on ecological carrying capacity is to settle the contradictions between resource and environment, and it is a significant basis for realizing regional sustainable development. This paper, on the basis of the academician Sun Tiehang's "unification of three" for the eco-city construction, established ecological carrying capacity evaluation indexes for the traditional industrial and mining city—Huainan City; and applied GM–BP neural network coupling model for the dynamic evolution and prediction of ecological carrying capacity of Huainan City in the future decade. The results showed that ecological carrying capacity index of Huainan would be 2.13 by 2025, higher than the loadable state 1, so the ecological carrying capacity would keep in the over-loaded level, but the over-loaded degree would be lower than the current. Carrying capacity of arable land, energy and water resources contribute greatly to the improvement of ecological carrying capacity, thus it is imperative to adjust this unreasonable and unsustainable ecological consumption relationship, enhance environmental protection awareness and high-efficiency utilization of resources, and take an energy-saving and intensive development path.

Structure analysis of shale and prediction of shear wave velocity based on petrophysical model and neural network

Accurate shear wave velocity is very important for seismic inversion.However,few researches in the shear wave velocity in organic shale have been carried out so far.In order to analyze the structure of organic shale and predict the shear wave velocity,the authors propose two methods based on petrophysical model and BP neural network respectively,to calculate shear wave velocity.For the method based on petrophysics model,the authors discuss the pore structure and the space taken by kerogen to construct a petrophysical model of the shale,and establish the quantitative relationship between the P-wave and S-wave velocities of shale and physical parameters such as pore aspect ratio,porosity and density.The best estimation of pore aspect ratio can be obtained by minimizing the error between the predictions and the actual measurements of the P-wave velocity.The optimal porosity aspect ratio and the shear wave velocity are predicted.For the BP neural network method that applying BP neural network to the shear wave prediction,the relationship between the physical properties of the shale and the elastic parameters is obtained by training the BP neural network,and the P-wave and S-wave velocities are predicted from the reservoir parameters based on the trained relationship.The above two methods were tested by using actual logging data of the shale reservoirs in the Jiaoshiba area of Sichuan Province.The predicted shear wave velocities of the two methods match well with the actual shear wave velocities,indicating that these two methods are effective in predicting shear wave velocity.

基于拌和生产数据的BP神经网络混凝土抗压强度预测

为解决混凝土生产中抗压强度试验周期长及工程管理存在滞后性的问题,提出了一种基于混凝土拌和生产实时监控数据的BP神经网络混凝土抗压强度预测模型。以混凝土拌和生产中的8项物料生产称重数据和5项生产配比数据作为预测输入变量,建立200组混凝土拌和站生产监控数据和对应的抗压强度试验数据样本集,按照6∶2∶2比例划分为训练集、验证集和测试集;分别以C40配比混凝土拌和生产的8项物料称重数据和全部13项数据作为输入变量,进行混凝土28 d抗压强度预测,将预测结果与实际试验结果进行比较,验证所提出BP神经网络模型的预测效果。结果表明:所提出的BP神经网络混凝土强度预测模型能较好地实时预测混凝土28 d抗压强度,且相对误差优于利用7 d抗压强度试验数据估算值;8项物料称重数据作为输入变量的BP神经网络预测模型预测精度更好,平均绝对百分比误差为0.82%,均方根误差为0.52 MPa;利用不同拌和站C20配比、C30配比混凝土拌和生产监控数据对8项输入变量BP神经网络混凝土抗压强度预测模型进行适应性验证可知,其预测平均绝对误差均在0.5 MPa之内,平均绝对百分比误差均小于2%,与C40配比预测误差一致;该预测模型充分挖掘了混凝土拌和站生产实时监控数据的价值,实现了传统混凝土抗压试验结果提前化,对提高工程建设质量水平具有重要意义。

基于PCA-BP神经网络的巷道通风摩擦阻力系数预测模型

根据实测巷道通风摩擦阻力系数数据的特点,建立了主成分分析PCA-BP神经网络预测模型。采用PCA法对影响巷道通风摩擦阻力系数的支护类型、断面形状、巷道宽、巷道高、支护部分周边长、巷道断面积和巷道长度7个因素进行降维。将降维后因素的贡献率进行排序筛选,得到3个主成分指标(F_(1)、F_(2)和F_(3)),作为BP神经网络输入层的神经元。利用实测数据对PCA-BP神经网络模型进行训练和测试,并将测试结果与支持向量机回归(SVM)模型和BP神经网络模型的测试结果进行对比,结果显示:全因素的BP神经网络预测模型和SVM预测模型的平均精度分别为92.9420%、93.0235%,而PCA-BP预测模型的平均精度达到了96.4325%。PCA-BP神经网络模型不但简化了网络结构,更提高了网络的泛化能力,使预测误差更小、精度更高,为更准确地获得巷道通风摩擦阻力系数提供了一种有效的方法。

基于BP神经网络的集中供热二次网回水温度预测控制研究

针对集中供热系统二次管网存在的水力失调问题,设计了二次网水力平衡调节及回水温度预测模型,并实施智能控制策略,以实现二次网回水温度的精准控制。首先,构建BP神经网络预测模型,将此模型的输出视为二次网回水温度给定值;其次,在整个系统控制中,实施BP神经网络与PID控制器相结合的策略,进行二次网回水温度的控制。以高邑县某小区换热站数据为基础,通过阶跃响应曲线法建立二次网回水温度控制系统的数学模型,并通过BP-PID控制进行仿真实验。实验结果表明,与传统PID控制器相比,BP-PID控制器具有调节时间短、超调量小的优点,能够快速达到平稳状态。

基于遗传算法和BP神经网络的矿区土壤重金属含量空间分布预测

本研究提出了一种基于遗传算法(Genetic algorithm,GA)和BP神经网络(Back propagation neural network,BPNN)的复合模型——GABP模型,以安徽省池州市某矿区及其周边为研究区,预测了土壤中p H和7种重金属元素(Cd、Pb、Cr、Cu、Ni、Hg、As)含量的空间分布,并与BPNN和反比距离权重法(Inverse distance weighting,IDW)进行了比较。研究结果表明:受采矿活动影响,研究区土壤p H和重金属含量呈显著的空间分异性;GABP复合模型的数据扩增能够有效弥补BPNN对样本数量的依赖,同时结合了地理位置和高程属性,精度评价结果显示GABP模型的平均R^(2)、r、RMSE、MAE分别是IDW和BPNN的3.03倍、2.56倍,2.93倍、2.39倍,0.85倍、0.61倍,0.79倍、0.62倍,预测精度更高。模型解决了传统空间插值方法结果中可能出现负值和边界无法插值的问题,为土壤重金属含量空间分布预测提供了一种新方法。

基于WOA-BP算法的自动压滤机脱水指标预测模型研究

随着全球工业化的不断发展,矿山的开采规模正在不断扩大,导致矿物资源逐渐贫化,细杂等难选矿物资源越来越多。选矿磨矿粒度越来越细,导致矿物分选后产品的脱水过滤越来越困难。为保证后续运输和冶炼工序对精矿含水率的生产需求,需要使用自动压滤机对精矿进行高效率的脱水处理。在精矿的过滤脱水过程中,影响自动压滤机脱水效率的因素众多。为更好地对脱水过程及生产指标进行控制,基于鲸鱼算法WOA优化的BP神经网络构建了一种WOA-BP神经网络模型,以入料浓度、入料时间、压榨时间、风干时间等4项影响脱水指标的因素为输入因子,以滤饼含水率和单位面积每小时处理量为输出因子,建立了脱水指标的预测模型,并对比分析单一BP神经网络模型和WOA-BP神经网络模型。结论如下:WOA-BP预测模型对滤饼含水率和单位面积每小时处理量的平均绝对误差分别为4.98%、8.83%,均方根误差分别为0.86%、3.43%,与单一的BP神经网络预测模型相比,该预测模型预测误差明显小于单一BP神经网络预测模型,脱水指标的预测结果更接近实测值,具有较高精确度。利用构建的WOA-BP预测模型,可以有效预测压滤机的脱水过滤指标,为后续对脱水过程的控制进行优化奠定了基础。

M-CM-GA-BP算法的地表移动变形参数预测模型

针对复杂的开采沉陷预测问题,研究22个工作面采动地表移动变形参数变化规律,提出了一种基于M-CM-GA-BP算法求取地表移动变形参数的预测模型。通过线性加权组合预测方法和遗传算法优化BP神经网络的权值和阈值,融合多元回归模型来提高地表移动变形参数的求取精度,以地表下沉系数q为例,将该模型与其他预测模型预测性能进行对比分析,验证模型的准确性。结果表明,该模型能够有效地提高地表移动变形参数的预测精度,模型的平均相对误差为1.294、均方根误差为0.013,为地表移动变形参数预测提供了一种可行方法。

基于改进PSO-BP神经网络的热采管柱应力预测

稠油热采过程中,油套管柱由于在温度、地层等多重载荷作用下发生塑性形变进而导致断裂或失效。文中根据热采管柱高温服役工况,引入异步变化学习因子和自适应权重建立输入参数为注汽温度、井深、非均匀系数和水泥环温度,输出参数为套管应力的改进PSO-BP模型。文中以N80热采套管为例,选取260、280、300、320、340℃5种温度工况下有限元模拟结果作为训练数据,对比BP模型、GA-BP模型、MEA-BP模型、PSO-BP模型和改进PSO-BP模型在300℃工况温度下井深200、300、400、500、600、700 m处套管应力的预测值和试验值、有限元计算值。结果表明:改进PSO-BP模型预测的应力与试验值最接近,最大和最小误差分别为2.69%和0.06%。最后从训练数据、预测误差、计算时间等方面对建立的改进PSO-BP模型进行了评价,为热采管柱服役过程中的强度安全分析提供智能高效的模型。

基于FOA-BP-AdaBoost的大坝变形预测模型及应用

为提升大坝变形监测预测精度,解决变形量受多因素影响等问题,笔者提出了基于果蝇优化算法(FOA)、BP神经网络的AdaBoost强预测组合模型(FOA-BP-AdaBoost),并与BP神经网络模型、FOA-BP神经网络模型应用于工程实例中的预测精度进行多方位量化对比。结果表明:强预测模型集齐了果蝇算法全局优化、BP神经网络局部寻优和AdaBoost“优中选优”的特点,最大程度优化了预测效果;实例应用证实了FOA-BP-AdaBoost模型在大坝变形预测领域的准确性和有效性。该模型已成功应用于工程实例,可为类似工程提供参考。

日光温室环境因子预测模型及应用——基于BP神经网络

为探讨北方日光温室内空气温湿度的变化规律,预测其变化趋势,进而确定合理的调控措施,采用L-M算法建立BP神经网络预测模型;选择S型函数作为网络激活函数,建立一种适用于北方日光温室空气温湿度环境因子的模拟预测模型。选取正常生产的日光温室为试验基地进行数据采集,采用皮尔逊相关系数确定模拟预测模型的输入因子,从1个月1440组实测数据中选取前29天的数据进行训练,对最后一天预测出的数据进行验证。研究结果表明:分段预测的预测值与实测值的符合度值大于全天预测,且分段预测的符合度大于0.99,均方根误差小于0.4,模型可用于模拟和预测北方日光温室大棚内空气温度与湿度的变化趋势,具有良好效果。

BP神经网络在混凝土性能预测中的应用

综述了BP神经网络在混凝土抗压强度及其他力学性能、耐久性、混凝土结构构件相关性能预测中的应用。综述表明,模型误差在工程可控范围内,模型泛化能力较强,利用BP神经网络对混凝土性能开展预测是可行的。

基于BP-ANN与RBF-ANN的钢筋与混凝土黏结强度预测模型研究

为研究神经网络对钢筋与混凝土黏结强度的预测能力以及神经网络的输出性能,基于大量的试验数据,提出一种基于改进神经网络的变形钢筋与混凝土黏结强度预测模型,对混凝土结构的研究与实际工程应用均有着重要的意义。收集290组黏结锚固试验数据,引入基于反向传播人工神经网络(BP-ANN)与径向基函数神经网络(RBF-ANN)算法,揭示混凝土强度、保护层厚度、钢筋直径、锚固长度及配箍率对变形钢筋与混凝土黏结性能的影响规律,建立基于改进神经网络算法的钢筋与混凝土黏结强度预测模型。对比分析不同数据预处理方法和训练神经元个数对建议模型预测结果的影响,评估各经典模型与建议模型的预测精度和离散性,提出临界锚固长度计算公式。结果表明:BP-ANN预测值与试验值比值的均值、标准差及变异系数分别为1.009、0.188、0.86,其预测精度略高于RBF-ANN;建议模型能够更准确、更稳定地预测钢筋与混凝土的黏结强度,该方法为解决钢筋与混凝土黏结问题提供了新思路。

基于SSA-BP的露天矿山边坡位移变形预测

针对传统的BP神经网络在预测露天矿山边坡位移变形时存在的局限性,构造了一种基于麻雀搜索算法(SSA)和BP神经网络相结合的边坡位移变形预测模型,先利用麻雀搜索算法对传统的BP神经网络进行权值与阈值的优化,再将麻雀搜索算法优化后的BP神经网络算法(SSA-BP)运用于露天矿山边坡位移的预测。为了验证算法的可行性,将SSA-BP预测模型与WOA-BP、BP以及Elman预测模型针对露天矿山边坡位移变形量的预测结果进行比较。实验结果表明:SSA-BP预测模型针对露天矿山边坡位移变形量的预测相比其他三种模型,其迭代速度快、寻优能力强;通过预测精度评价指标来看,SSA-BP算法的R2、RMSE、MAPE、MAE、MSE明显优于另外三组算法。为露天矿山边坡位移变形预测提供了一种新的思路和方法。

基于BP神经网络的乒乓球优秀女子单打比赛结果预测模型构建及仿真应用

为构建优秀女子运动员技战术表现与比赛获胜的理论模型,合理安排技战术训练及比赛策略提供参考借鉴。运用录像观察法、数理统计法及BP神经网络构建乒乓球优秀女子单打比赛结果预测模型。在此基础上,利用预测模型仿真分析新型塑料球时代优秀女子乒乓球运动员在不同水平技战术组合下的比赛获胜模式。本文构建的预测模型R为0.978,R~2为0.956,平均绝对误差为0.0085,模型精度达到98.4%;仿真分析1 024种技战术段组合结果可知,568种组合的预测结果为获胜,456种组合的预测结果为失败。结论:基于BP神经网络构建的乒乓球优秀女子单打比赛结果预测模型拟合效果佳,个案实证预测效果较好,具有较高的预测性能;新型塑料球时代优秀女子运动员在单打比赛中,各技战术段之间的补偿效应因技战术段和等级而有所不同,评估总分17分为女子单打比赛胜负的分界点,不同水平技战术段组合的比赛评估总分大于17分即可取得比赛胜利,低于17分则会落败。

Development of viscosity model for aluminum alloys using BP neural network

Viscosity is one of the important thermophysical properties of liquid aluminum alloys,which influences the characteristics of mold filling and solidification and thus the quality of castings.In this study,315 sets of experimental viscosity data collected from the literatures were used to develop the viscosity prediction model.Back-propagation(BP)neural network method was adopted,with the melt temperature and mass contents of Al,Si,Fe,Cu,Mn,Mg and Zn solutes as the model input,and the viscosity value as the model output.To improve the model accuracy,the influence of different training algorithms and the number of hidden neurons was studied.The initial weight and bias values were also optimized using genetic algorithm,which considerably improve the model accuracy.The average relative error between the predicted and experimental data is less than 5%,confirming that the optimal model has high prediction accuracy and reliability.The predictions by our model for temperature-and solute content-dependent viscosity of pure Al and binary Al alloys are in very good agreement with the experimental results in the literature,indicating that the developed model has a good prediction accuracy.

基于SSA-BP的爆破振动峰值速度预测研究

为了精准预测爆破振动峰值速度(PPV),有效降低爆破振动的危害,以星光一号露天矿山爆破工程为依托,选取爆心距、堵塞长度、最小抵抗线、炸药单耗、最大单孔装药量、总延期时间、最大单响药量等7个影响因素作为输入变量,采用灰色关联分析法评估各因素与PPV之间的相关性,构建麻雀搜索算法(SSA)优化BP神经网络的爆破峰值振速预测模型,对三向峰值振动速度进行预测,并与BP神经网络模型预测结果进行对比分析,得到SSA-BP神经网络模型预测结果的平均误差分别为6.08%、7.34%、1.91%,BP神经网络模型预测结果的平均误差分别为22.19%、54.01%、25.29%。研究结果表明:SSA-BP神经网络模型全面考虑了多种爆破设计参数对振动峰值速度的影响;麻雀搜索优化算法有效解决了传统BP神经网络模型容易陷入局部最优的问题,预测结果更精确,与振速监测值吻合度更高、误差更小;并且极大地缩短了样本数据的学习训练时间,加快BP神经网络预测模型的收敛速度,可为类似露天爆破工程质点峰值振速的预测提供借鉴。

BP neural network model on the forecast for blasting vibrating parameters in the course of hole-by-hole detonation

According to the neural network theory, combined with the technical characteristicsof the hole-by-hole detonation technology, a BP network model on the forecast forblasting vibration parameters was built.Taking the deep hole stair demolition in a mine asan experimental object and using the raw information and the blasting vibration monitoringdata collected in the process of the hole-by-hole detonation, carried out some training andapplication work on the established BP network model through the Matlab software, andachieved good effect.Also computed the vibration parameter with the empirical formulaand the BP network model separately.After comparing with the actual value, it is discoveredthat the forecasting result by the BP network model is close to the actual value.