Intelligent predictive model of ventilating capacity of imperial smelt furnace

In order to know the ventilating capacity of imperial smelt furnace(ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in which the weight values in the integrated model can be adjusted automatically. An intelligent predictive model of the ventilating capacity of the ISF is established and analyzed by the method. The simulation results and industrial applications demonstrate that the predictive model is close to the real plant, the relative predictive error is 0.72%, which is 50% less than the single model, leading to a notable increase of the output of plumbum.

Forecasting Alzheimer’s Disease Using Combination Model Based on Machine Learning

As the acceleration of aged population tendency, building models to forecast Alzheimer’s Disease (AD) is essential. In this article, we surveyed 1157 interviewees. By analyzing the results using three machine learning methods—BP neural network, SVM and random forest, we can derive the accuracy of them in forecasting AD, so that we can compare the methods in solving AD prediction. Among them, random forest is the most accurate method. Moreover, to combine the advantages of the methods, we build a new combination forecasting model based on the three machine learning models, which is proved more accurate than the models singly. At last, we give the conclusion of the connection between life style and AD, and provide several suggestions for elderly people to help them prevent AD.

Combined back-analysis method of ground stress based on refined geological modeling

A new back-analysis method of ground stress is proposed with comprehensive consideration of influence of topography, geology and nonlinear physical mechanical properties of rock on ground stress. This method based on non-uniform rational B-spline (NURBS) technology provides the means to build a refined three-dimensional finite element model with more accurate meshing under complex terrain and geological conditions. Meanwhile, this method is a back-analysis of ground stress with combination of multivariable linear regression model and neural network (ANN) model. Firstly, the regression model is used to fit approximately boundary loads. Regarding the regressed loads as mean value, some sets of boundary loads with the same interval are constructed according to the principle of orthogonal design, to calculate the corresponding ground stress at the observation positions using finite element method. The results (boundary loads and the corresponding ground stress) are added to the samples for ANN training. And on this basis, an ANN model is established to implement higher precise back-analysis of initial ground stress. A practical application case shows that the relative error between the inversed ground stress and observed value is mostly less than 10 %, which can meet the need of engineering design and construction requirements.

某纯电驱动重载车辆能耗预测模型

高精度能耗预测模型是准确预测车辆续驶里程的重要前提。针对载荷大幅度变化且非结构化道路运行的纯电驱动重载车辆,建立其组合能耗模型,该模型由能耗计算基本模型与长短时记忆(Long Short-Term Memory,LSTM)神经网络差值修正两部分组成。基于能量流动过程驱动电机和变速器效率建模,结合汽车行驶动力学建立能耗计算基本模型;采用LSTM神经网络来修正基本模型能耗预测结果与车辆典型工况功率测试值的差值,有效提高了大幅变载荷且低信噪比坡度环境下的车辆能耗预测精度,因此组合能耗模型具有参数简单和模型拟合不需解释能耗规律的优点。经试验测试分析,与VT-Micro能耗模型和径向基(Radial Basis Function,RBF)神经网络能耗模型相比,所提组合能耗模型的功率预测平均误差率分别降低了17.76%和3.35%,能够实现纯电驱动重载车辆复杂工况下能耗的准确实时预测。

白条猪价格预测模型构建

【目的】增强农产品价格预测准确度,为农产品价格的有效预测提供参考。【方法】以河南省白条猪每周平均批发价格为研究对象,提出一种基于序列分解、主成分分析和神经网络(CEEMDAN-PCA-CNN-LSTM)的白条猪价格预测方法。首先,使用自适应白噪声完全集合模态分解方法(CEEMDAN)对白条猪价格序列进行分解;其次,选用皮尔逊相关系数筛选影响价格波动的相关因素;再次,利用主成分分析(PCA)对影响因素及分解得到的子序列降维处理并作为原始价格序列的特征值,并行输入到作为编码器的卷积神经网络(CNN)中进行特征提取;最后,引入长短期记忆网络(LSTM)作为解码器输出得到预测结果。将该方法应用于河南省白条猪每周平均价格数据,与LSTM、门控循环单元(GRU)、CNN、基于卷积的长短期记忆网络(ConvLSTM)模型进行比较。【结果】CEEMDAN-PCA-CNN-LSTM组合模型预测方法得到的平均绝对误差分别降低了44.95%、27.30%、28.13%、43.17%。【结论】CEEMDAN-PCA-CNN-LSTM模型对于河南省白条猪市场价格的预测性能更优,有助于相关部门针对河南省白条猪价格波动做出科学决策。

ML组合的CYGNSS海面风速反演质量控制模型

卷积神经网络(CNN)可用于气旋全球导航卫星系统(CYGNSS)的海面风速反演。虽然在模型训练前设置了质量控制指标来检测和削弱CYGNSS的异常观测数据,但CYGNSS观测数据中仍存在异常值导致模型反演精度降低,甚至出现错误反演结果。因此,提出一种基于机器学习(ML)组合的海面风速反演模型。在基于CNN回归模型的CYGNSS反演海面风速基础上,ML分类模型生成CNN回归结果的质量标志位,该标志位可以检测并删除CNN回归结果的异常值,进一步提高风速反演结果的数据质量,ML分类模型能够更好地考虑各种数据误差之间的相互作用,而不是单独使用每个条件的阈值,以达到更优的海面风速反演精度的效果。实验对比了Logistic回归(LR)、决策树(DT)、朴素贝叶斯模型、K最邻近(KNN)算法、神经网络(NN)模型、支持向量机(SVM)算法等6个分类模型,其中,基于KNN算法的分类模型对风速反演质量控制的效果最优。所提风速反演组合模型显著提高了反演结果的精度,在0~20 m/s区间内,异常样本过滤率为81.27%,在所有被过滤的数据中,过滤正确率为86.03%;风速反演误差的均方根误差从无ML分类模型的1.7 m/s降低到有ML分类模型的1.44 m/s,其中,训练样本为0~10 m/s的反演结果精度提升效果较为明显,证明了所提风速反演组合模型对风速质量控制的有效性。

SARIMA-GRU组合模型的水位预测

相较于传统的单一模型,组合模型在一定条件下具有更优的预测精度。为验证组合模型是否有利于提高模型的预测精度,本文以长江中游支流澧水石龟山水电站的水位数据为基础,建立SARIMA模型和GRU神经网络模型,并将这两种模型基于方差倒数法和IOWA算子进行组合,最后比较单一模型和组合模型在该水位数据集上的预测精度差异。结果表明,适当的组合方式有利于提高模型预测精度,基于IOWA算子的组合模型具优良的预测性能。

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。

Establishment of Neural Network Prediction Model for Terminative Temperature Based on Grey Theory in Hot Metal Pretreatment

In order to improve the accuracy of model for terminative temperature in steelmaking, it is necessary to predict and control before decarburization. Thus, an optimization neural network model of terminative temperature in the process of dephosphorization by laying correlative degree weights to all input factors related was used. Then sim- ulation experiment of model newly established is conducted utilizing 210 data from a domestic steel plant. The results show that hit rate arrives at 56.45~~ when error is within plus or minus 5%, and the value is 100% when within ~10%. Comparing to the traditional neural network prediction model, the accuracy almost increases by 6. 839o//oo. Thus, the simulation prediction fits the real perfectly, which accounts for that neural network model for terminative tempera- ture based on grey theory can reflect accurately the practice in dephosphorization. Naturally, this method is effective and nraeticahle.

42CrMo钢精密切削的刀具磨损量预测研究

针对42CrMo钢精密切削刀具磨损量预测研究小样本、非线性的特点,将量子粒子群算法(QPSO)、卷积神经网络(CNN)及长短期神经网络(LSTM)相结合,构建了QPSO-CNN-LSTM组合预测模型。采用QPSO算法对CNN-LSTM模型的隐藏层单元数、学习率、卷积核等进行优化,结合CNN网络特征提取能力强、LSTM网络具备记忆能力的特点,对实际加工实验的刀具磨损量进行预测,并通过误差评价指标分析,与CNN、LSTM、BP等单一模型以及PSO-GRNN组合模型进行预测效果对比研究。研究结果表明,本文构建的组合预测模型相对于单一预测模型,其预测值与真实值吻合程度更高;相对于PSO-GRNN组合模型,三种误差评价指标的误差值至少降低了27%,其泛化性和稳定性较好,预测精度与非线性拟合能力更强。

基于LSTM人工神经网络的电力系统负荷预测方法

针对电力市场环境下短期电力系统负荷预测准确性较低的问题,提出了一种基于LSTM人工神经网络的组合预测模型。分析了LSTM神经网络和其变体GRU神经网络在进行负荷预测时学习时序特征的独特优势,并以卷积神经网络作为负荷数据的特征提取层,结合GRU网络构建了组合模型,通过建立残差预测模型对结果进行修正。仿真结果表明,具有记忆功能的神经网络预测效果要优于ANN和SVM模型,且所提出残差预测模型的负荷预测平均相对误差约为1.79%,其准确性高于单一算法的负荷预测模型。

基于PSO-RBF组合模型的长江集装箱运价指数预测

长江集装箱运价指数作为长江航运市场的晴雨指向标,能够有效反映中国长江航运的经济情况,同时侧面反映出中国内河航运的发展动态。通过对长江集装箱运价指数的预测,可以为沿岸航运企业经营决策和政府宏观经济制定提供重要依据。选取影响长江集装箱运价指数的8个指数,运用BP神经网路、RBF神经网络对2017年至2022年5月长江集装箱运价指数进行预测,提出了一种改进的PSO-RBF组合模型,获得的预测误差较小。结果表明:粒子群算法能对RBF神经网络的输出权重、隐单元中心等关键参数取值进行寻优,使其能够更好地收敛,结果优于其他算法;PSO-RBF组合模型是预测长江集装箱运价指数的一种有效方法。

基于GM-RBF组合模型的BDS-3卫星钟差短期预报

针对卫星钟差具有趋势项和随机项变化的特征问题,提出了GM-RBF组合模型的方法。该模型首先用GM(1,1)提取预处理后的卫星钟差趋势项部分并进行建模预报,得到相应的残差序列,通过RBF神经网络训练用灰色模型预报所获得的残差序列,然后将GM(1,1)模型的钟差后续预报值与RBF神经网络的残差预报值对应相加可得组合模型的预报结果。为验证组合模型的有效性和可行性,将组合模型预报结果与GM(1,1)模型、ARIMA模型、RBF神经网络模型预报结果进行对比实验。实验结果表明:组合模型预报精度要高于其他单一模型,其在不同时段的平均预报精度可提高46.4%~86.2%。

基于VMD的长江航运干散货运价指数预测

我国长江航运干散货运价指数(YBFI)呈现非线性、非平稳性等波动特征,传统的单一预测模型和组合预测法难以获得较好的预测效果。为此,基于“分解-重构-预测-集成”的思想,提出一种基于变分模态分解(VMD)的YBFI组合预测模型构建方法。选用变分模态分解(VMD)将原始运价指数序列分解为多个模态分量,并通过聚类分析将分量重构为高频、中频、低频和趋势项,对重构后的序列波动特点进行解释。选用BPNN对高频项和低频项进行预测,采用PSO-SVM方法对中频项和趋势项进行预测,最后将重构项预测结果相加集成得到最终预测值。实证结果表明,构建的基于VMD的组合预测模型比SVM、BPNN、ARIMA、PLS等单一预测模型,以及未优化的VMD组合模型、VMD-BP等组合模型具有更好的预测效果。

基于加权组合模型的二手车价格预测方法

为了提高二手车价格预测的精度与可靠性,提出了一种基于神经网络与贝叶斯优化LightGBM算法的加权组合方法,对具有多个影响因素的二手车价格进行预测。首先对原始数据进行数据预处理,其次对预处理后的数据集进行特征工程以获得适合神经网络与树模型训练的数据集,然后分别使用神经网络与贝叶斯优化的若干机器学习算法进行训练获得网络模型,最后将各模型进行组合并与单一模型进行对比。预测结果显示,提出的神经网络与改进LightGBM算法的加权组合模型比单一模型以及其他组合方式的模型预测能力更强。

基于深度学习的股票价格指数预测

文章以2012—2022年上证50、沪深300、中证500三支股指交易数据为研究对象,利用深度学习预测股指,分别构建了卷积神经网络(CNN)模型、长短期记忆神经网络(LSTM)模型和CNN-LSTM组合神经网络模型,使用均方根误差(RMSE)、平均绝对误差(MAE)、平均百分比误差绝对值(MAPE)和决定系数(R2)4个指标对模型进行评价。结果表明,组合模型兼有CNN模型的高维特征挖掘优势和LSTM模型的时序特征提取优势,在3支股指的预测中,组合模型的预测精度均高于CNN模型和LSTM模型单一模型,其RMSE指标分别提升了28.60百分点、52.56百分点和25.28百分点,证实了CNN-LSTM组合神经网络模型的有效性和准确性。

基于交通小区短时交通生成预测的城市道路网短时动态交通预测研究

城市道路周边用地性质的不同导致短时发生和吸引交通量的差异,直接影响城市道路网上的交通流。论文对城市路网中各地块的发生和吸引交通量进行短时预测,将预测的交通量通过动态交通分配到城市路网中,以此实现城市路网短时动态交通预测。将单一的BP神经网络模型为对比模型,对模型进行训练及参数标定,并检验模型的预测效果。实验结果表明,论文所提的预测方法与BP神经网络相比,预测精度最高可提升84.28%。

Artificial neural network-based repair and maintenance cost estimation model for rice combine harvesters

This research proposes an artificial neural network(ANN)-based repair and maintenance(R&M)cost estimation model for agricultural machinery.The proposed ANN model can achieve high estimation accuracy with small data requirement.In the study,the proposed ANN model is implemented to estimate the R&M costs using a sample of locally-made rice combine harvesters.The model inputs are geographical regions,harvest area,and curve fitting coefficients related to historical cost data;and the ANN output is the estimated R&M cost.Multilayer feed-forward is adopted as the processing algorithm and Levenberg-Marquardt backpropagation learning as the training algorithm.The R&M costs are estimated using the ANN-based model,and results are compared with those of conventional mathematical estimation model.The results reveal that the percentage error between the conventional and ANN-based estimation models is below 1%,indicating the proposed ANN model’s high predictive accuracy.The proposed ANN-based model is useful for setting the service rates of agricultural machinery,given the significance of R&M cost in profitability.The novelty of this research lies in the use of curve-fitting coefficients in the ANN-based estimation model to improve estimation accuracy.Besides,the proposed ANN model could be further developed into web-based applications using a programming language to enable ease of use and greater user accessibility.Moreover,with minor modifications,the ANN estimation model is also applicable to other geographical areas and tractors or combine harvesters of different countries of origin.

High-resolution peak demand estimation using generalized additive models and deep neural networks

This paper covers predicting high-resolution electricity peak demand features given lower-resolution data.This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available.That question is particularly interesting for network operators considering replacing high-resolution monitoring by predictive models due to economic considerations.We propose models to predict half-hourly minima and maxima of high-resolution(every minute)electricity load data while model inputs are of a lower resolution(30 min).We combine predictions of generalized additive models(GAM)and deep artificial neural networks(DNN),which are popular in load forecasting.We extensively analyze the prediction models,including the input parameters’importance,focusing on load,weather,and seasonal effects.The proposed method won a data competition organized by Western Power Distribution,a British distribution network operator.In addition,we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models’robustness.The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error(RMSE).This holds regarding the competition month and the supplementary evaluation study,which covers an additional eleven months.Overall,our proposed model combination reduces the out-of-sample RMSE by 57.4%compared to the benchmark.

Research on Hydrological Time Series Prediction Based on Combined Model

Water level prediction of river runoff is an important part of hydrological forecasting.The change of water level not only has the trend and seasonal characteristics,but also contains the noise factors.And the water level prediction ability of a single model is limited.Since the traditional ARIMA(Autoregressive Integrated Moving Average)model is not accurate enough to predict nonlinear time series,and the WNN(Wavelet Neural Network)model requires a large training set,we proposed a new combined neural network prediction model which combines the WNN model with the ARIMA model on the basis of wavelet decomposition.The combined model fit the wavelet transform sequences whose frequency are high with the WNN,and the scale transform sequence which has low frequency is fitted by the ARIMA model,and then the prediction results of the above are reconstructed by wavelet transform.The daily average water level data of the Liuhe hydrological station in the Chu River Basin of Nanjing are used to forecast the average water level of one day ahead.The combined model is compared with other single models with MATLAB,and the experimental results show that the accuracy of the combined model is improved by 7%compared with the traditional wavelet network under the appropriate wavelet decomposition function and the combined model parameters.