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Predictive modeling for mechanical properties of cold-rolled strip steel based on random forest regression and whale optimization algorithm 认领 引用
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作者 Hong-Lei Cai Yi-Ming Fang +3 位作者 Le Liu Li-Hui Ren Zhen-Dong Liu Xiao-Dong Zhao 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第3期73-87,共15页
In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method n... In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method named RFR-WOA is developed based on random forest regression(RFR)and whale optimization algorithm(WOA).Firstly,using Pearson and Spearman correlation analysis and Gini coefficient importance ranking on an actual production dataset containing 37,878 samples,22 key variables are selected as model inputs from 112 variables that affect mechanical properties.Subsequently,an RFR-based predictive model for the mechanical properties of cold-rolled strip steel is constructed.Then,with the combination of the coefficient of determination(R2)and root mean square error as the optimization objective,the hyperparameters of RFR model are iteratively optimized using WOA,and better predictive effectiveness is obtained.Finally,the mechanical properties prediction model based on RFR-WOA is compared with models established using deep neural networks,convolutional neural networks,and other methods.The test results on 9469 samples of actual production data show that the model developed present has better predictive accuracy and generalization capability. 展开更多
关键词 Cold-rolled strip steel Mechanical property Predictive modeling Random forest regression Whale optimization algorithm
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Random forest algorithm reveals novel sites in HA protein that shift receptor binding preference of the H9N2 avian influenza virus 认领 引用 被引量：2
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作者 Yuncong Yin Wen Li +7 位作者 Rujian Chen Xiao Wang Yiting Chen Xinyuan Cui Xingbang Lu David M.Irwin Xuejuan Shen Yongyi Shen 《Virologica Sinica》 SCIE CAS CSCD 2025年第1期109-117,共9页
A switch from avian-typeα-2,3 to human-typeα-2,6 receptors is an essential element for the initiation of a pandemic from an avian influenza virus.Some H9N2 viruses exhibit a preference for binding to human-typeα-2,... A switch from avian-typeα-2,3 to human-typeα-2,6 receptors is an essential element for the initiation of a pandemic from an avian influenza virus.Some H9N2 viruses exhibit a preference for binding to human-typeα-2,6 receptors.This identifies their potential threat to public health.However,our understanding of the molecular basis for the switch of receptor preference is still limited.In this study,we employed the random forest algorithm to identify the potentially key amino acid sites within hemagglutinin(HA),which are associated with the receptor binding ability of H9N2 avian influenza virus(AIV).Subsequently,these sites were further verified by receptor binding assays.A total of 12 substitutions in the HA protein(N158D,N158S,A160 N,A160D,A160T,T163I,T163V,V190T,V190A,D193 N,D193G,and N231D)were predicted to prefer binding toα-2,6 receptors.Except for the V190T substitution,the other substitutions were demonstrated to display an affinity for preferential binding toα-2,6 receptors by receptor binding assays.Especially,the A160T substitution caused a significant upregulation of immune-response genes and an increased mortality rate in mice.Our findings provide novel insights into understanding the genetic basis of receptor preference of the H9N2 AIV. 展开更多
关键词 H9N2 Hemagglutinin(HA) Receptor binding preference Random forest algorithm Host shift Interspecies transmission
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Prostate cancer prediction forest algorithm that takes using the random into account transrectal ultrasound findings, age, and serum levels of prostate-specific antigen 认领 引用 被引量：7
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作者 Li-Hong Xiao Pei-Ran Chen +4 位作者 Zhong-Ping Gou Yong-Zhong Li Mei Li Liang-Cheng Xiang Ping Feng 《Asian Journal of Andrology》 SCIE CAS CSCD 2017年第5期586-590,共5页
The aim of this study is to evaluate the ability of the random forest algorithm that combines data on transrectal ultrasound findings, age, and serum levels of prostate-specific antigen to predict prostate carcinoma. ... The aim of this study is to evaluate the ability of the random forest algorithm that combines data on transrectal ultrasound findings, age, and serum levels of prostate-specific antigen to predict prostate carcinoma. Clinico-demographic data were analyzed for 941 patients with prostate diseases treated at our hospital, including age, serum prostate-specific antigen levels, transrectal ultrasound findings, and pathology diagnosis based on ultrasound-guided needle biopsy of the prostate. These data were compared between patients with and without prostate cancer using the Chi-square test, and then entered into the random forest model to predict diagnosis. Patients with and without prostate cancer differed significantly in age and serum prostate-specific antigen levels （P 〈 0.001）, as well as in all transrectal ultrasound characteristics （P 〈 0.05） except uneven echo （P = 0.609）. The random forest model based on age, prostate-specific antigen and ultrasound predicted prostate cancer with an accuracy of 83.10%, sensitivity of 65.64%, and specificity of 93.83%. Positive predictive value was 86.72%, and negative predictive value was 81.64%. By integrating age, prostate-specific antigen levels and transrectal ultrasound findings, the random forest algorithm shows better diagnostic performance for prostate cancer than either diagnostic indicator on its own. This algorithm may help improve diagnosis of the disease by identifying patients at high risk for biopsy. 展开更多
关键词 diagnosis prostate cancer prostate-specific antigen random forest algorithm transrectal ultrasound characteristics
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A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm 认领 引用 被引量：11
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作者 Tie Yan Rui Xu +2 位作者 Shi-Hui Sun Zhao-Kai Hou Jin-Yu Feng 《Petroleum Science》 SCIE EI CAS CSCD 2024年第2期1135-1148,共14页
Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face ... Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation. 展开更多
关键词 Intelligent drilling Closed-loop drilling Lithology identification Random forest algorithm Feature extraction
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Prediction of Permeability Using Random Forest and Genetic Algorithm Model 认领 引用 被引量：8
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作者 JunhuiWang Wanzi Yan +3 位作者 Zhijun Wan Yi Wang Jiakun Lv Aiping Zhou 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第12期1135-1157,共23页
Precise recovery of CoalbedMethane(CBM)based on transparent reconstruction of geological conditions is a branch of intelligent mining.The process of permeability reconstruction,ranging from data perception to real-tim... Precise recovery of CoalbedMethane(CBM)based on transparent reconstruction of geological conditions is a branch of intelligent mining.The process of permeability reconstruction,ranging from data perception to real-time data visualization,is applicable to disaster risk warning and intelligent decision-making on gas drainage.In this study,a machine learning method integrating the Random Forest(RF)and the Genetic Algorithm(GA)was established for permeability prediction in the Xishan Coalfield based on Uniaxial Compressive Strength(UCS),effective stress,temperature and gas pressure.A total of 50 sets of data collected by a self-developed apparatus were used to generate datasets for training and validating models.Statistical measures including the coefficient of determination(R2)and Root Mean Square Error(RMSE)were selected to validate and compare the predictive performances of the single RF model and the hybrid RF–GA model.Furthermore,sensitivity studies were conducted to evaluate the importance of input parameters.The results show that,the proposed RF–GA model is robust in predicting the permeability;UCS is directly correlated to permeability,while all other inputs are inversely related to permeability;the effective stress exerts the greatest impact on permeability based on importance score,followed by the temperature(or gas pressure)and UCS.The partial dependence plots,indicative of marginal utility of each feature in permeability prediction,are in line with experimental results.Thus,the proposed hybrid model(RF–GA)is capable of predicting permeability and thus beneficial to precise CBMrecovery. 展开更多
关键词 Permeability machine learning random forest genetic algorithm coalbed methane recovery
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Prediction of maximum upward displacement of shield tunnel linings during construction using particle swarm optimization-random forest algorithm 认领 引用 被引量：9
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作者 Xiaowei YE Xiaolong ZHANG +2 位作者 Yanbo CHEN Yujun WEI Yang DING 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2024年第1期1-17,共17页
During construction,the shield linings of tunnels often face the problem of local or overall upward movement after leaving the shield tail in soft soil areas or during some large diameter shield projects.Differential ... During construction,the shield linings of tunnels often face the problem of local or overall upward movement after leaving the shield tail in soft soil areas or during some large diameter shield projects.Differential floating will increase the initial stress on the segments and bolts which is harmful to the service performance of the tunnel.In this study we used a random forest(RF)algorithm combined particle swarm optimization(PSO)and 5-fold cross-validation(5-fold CV)to predict the maximum upward displacement of tunnel linings induced by shield tunnel excavation.The mechanism and factors causing upward movement of the tunnel lining are comprehensively summarized.Twelve input variables were selected according to results from analysis of influencing factors.The prediction performance of two models,PSO-RF and RF(default)were compared.The Gini value was obtained to represent the relative importance of the influencing factors to the upward displacement of linings.The PSO-RF model successfully predicted the maximum upward displacement of the tunnel linings with a low error(mean absolute error(MAE)=4.04 mm,root mean square error(RMSE)=5.67 mm)and high correlation(R2=0.915).The thrust and depth of the tunnel were the most important factors in the prediction model influencing the upward displacement of the tunnel linings. 展开更多
关键词 Random forest(RF) Particle swarm optimization(PSO) Upward displacement of lining Machine learning prediction Shieldtunneling construction
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基于RF-Transformer的测井曲线页岩岩相识别方法 认领 引用 被引量：1
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作者 苏俊磊 董旭 +4 位作者 唐嘉伟 曾渝 石雪莹 李佩璇 杨仁杰 《测井技术》 CAS 2026年第1期153-162,共10页
岩相识别是油气储层精细刻画的关键环节,其准确性直接影响储层评价结果的可靠性。现有识别方法在测井数据高频噪声抑制方面存在不足,且难以准确捕捉地层纵向长程依赖关系。因此,本文提出了一种融合随机森林(Random Forest,RF)与Transfor... 岩相识别是油气储层精细刻画的关键环节,其准确性直接影响储层评价结果的可靠性。现有识别方法在测井数据高频噪声抑制方面存在不足,且难以准确捕捉地层纵向长程依赖关系。因此,本文提出了一种融合随机森林(Random Forest,RF)与Transformer的深度学习模型(RF-Transformer),以提高非均质储层页岩岩相识别的准确性与效率,为储层精细刻画提供技术支撑。该模型首先利用随机森林模型评估测井曲线(如自然伽马、声波时差、电阻率等)特征权重,用以筛选关键参数进而压制高频噪声,构建高质量特征输入向量。随后用Transformer模块,借助其自注意力机制的全局上下文感知能力,并行计算测井曲线的关联权重,从而深度挖掘并重构地层纵向长程依赖关系。以川南页岩气田3800个实测样本(含6类典型岩相、8条常规测井曲线)为数据集,开展模型性能对比与实例应用分析。结果表明:①RF-Transformer模型准确率达91.51%,较Transformer、长短期记忆网络(Long Short-Term Memory,LSTM)和卷积神经网络(Convolutional Neural Network,CNN)模型分别提升了12.90%、23.60%和47.54%,优于K近邻(81.09%)、决策树(77.28%)等传统机器学习模型;②该模型仅需约25次迭代即可进入收敛态,收敛速度较现有模型提升8~10倍;③成功筛选出自然伽马、声波时差、浅侧向电阻率等6条关键测井曲线,有效剔除深侧向电阻率等冗余特征与非地质噪声;④实例应用中,预测页岩岩相剖面纵向连续性与平滑度高,与真实地质分层特征高度吻合,精准刻画页岩岩相过渡带边界。结论认为,该模型在兼顾高抗噪性与强时序捕捉能力的同时,实现页岩岩相的高效精准识别,为非均质储层精细描述提供了可靠技术支撑,后续需围绕测井解释软件适配性展开优化。 展开更多
关键词 测井曲线 岩相识别 随机森林(Random Forest,RF) 深度学习 Transformer 页岩储层 长程依赖 噪声抑制
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Underground location algorithm based on random forest and environmental factor compensation 认领 引用 被引量：5
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作者 Xin Qiao Fei Chang 《International Journal of Coal Science & Technology》 EI CAS CSCD 2021年第5期1108-1117,共10页
Aiming at the poor location accuracy caused by the harsh and complex underground environment,long strip roadway,limited wireless transmission and sparse anchor nodes,an underground location algorithm based on random f... Aiming at the poor location accuracy caused by the harsh and complex underground environment,long strip roadway,limited wireless transmission and sparse anchor nodes,an underground location algorithm based on random forest and compensation for environmental factors was proposed.Firstly,the underground wireless access point(AP)network model and tunnel environment were analyzed,and the fingerprint location algorithm was built.And then the Received Signal Strength(RSS)was analyzed by Kalman Filter algorithm in the offline sampling and real-time positioning stage.Meanwhile,the target speed constraint condition was introduced to reduce the error caused by environmental factors.The experimental results show that the proposed algorithm solves the problem of insufficient location accuracy and large fluctuation affected by environment when the anchor nodes are sparse.At the same time,the average location accuracy reaches three meters,which can satisfy the application of underground rescue,activity track playback,disaster monitoring and positioning.It has high application value in complex underground environment. 展开更多
关键词 Underground Coal Mine Random Forest Kalman Filter Compensation Algorithm
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Investigation of Nuclear Binding Energy and Charge Radius Based on Random Forest Algorithm 认领 引用 被引量：1
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作者 CAI Boshuai YU Tianjun +3 位作者 LIN Xuan ZHANG Jilong WANG Zhixuan YUAN Cenxi 《原子能科学技术》 EI CAS CSCD 北大核心 2023年第4期704-712,共9页
The random forest algorithm was applied to study the nuclear binding energy and charge radius.The regularized root-mean-square of error(RMSE)was proposed to avoid overfitting during the training of random forest.RMSE ... The random forest algorithm was applied to study the nuclear binding energy and charge radius.The regularized root-mean-square of error(RMSE)was proposed to avoid overfitting during the training of random forest.RMSE for nuclides with Z,N>7 is reduced to 0.816 MeV and 0.0200 fm compared with the six-term liquid drop model and a three-term nuclear charge radius formula,respectively.Specific interest is in the possible(sub)shells among the superheavy region,which is important for searching for new elements and the island of stability.The significance of shell features estimated by the so-called shapely additive explanation method suggests(Z,N)=(92,142)and(98,156)as possible subshells indicated by the binding energy.Because the present observed data is far from the N=184 shell,which is suggested by mean-field investigations,its shell effect is not predicted based on present training.The significance analysis of the nuclear charge radius suggests Z=92 and N=136 as possible subshells.The effect is verified by the shell-corrected nuclear charge radius model. 展开更多
关键词 nuclear binding energy nuclear charge radius random forest algorithm
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Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China 认领 引用 被引量：1
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作者 SHI Xiaoliang CHEN Jiajun +2 位作者 DING Hao YANG Yuanqi ZHANG Yan 《Chinese Geographical Science》 SCIE CSCD 2024年第2期342-356,共15页
Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous r... Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R2=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R2of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R2=0.680)and normal year(2017)(R2=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R2=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield. 展开更多
关键词 winter wheat yield estimation sparrow search algorithm combined with random forest(SSA-RF) machine learning multi-source indicator optimal lead time Henan Province,China
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Adaptive adjustment strategy for earth pressure balance(EPB)operation parameters for pre-control of shield tunneling-induced surface settlement:a case study 认领 引用
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作者 Dongsheng WEI Haibin WEI +3 位作者 Zipeng MA Heting WEI Lijie SUN Xiaokun YU 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第7期676-695,I0010-I0023,共20页
During shield tunneling,ground deformation poses significant safety risks.The full optimization strategy ignores interactions between parameters,resulting in suboptimal performance in the pre-control of settlement in ... During shield tunneling,ground deformation poses significant safety risks.The full optimization strategy ignores interactions between parameters,resulting in suboptimal performance in the pre-control of settlement in earth pressure balance shields.To address this problem,this paper proposes an integrated strategy that combines optimization and inversion,minimizing parameter interaction interference through adaptive adjustment of shield operation parameters.This mechanism performs an optimization search on the key operation parameters for settlement control,while the remaining operation parameters are predicted through inversion.Taking the Changchun Metro Line 6 project as an example,a bidirectional long short-term memory(Bi-LSTM)model enhanced by a multi-head self-attention(MHSA)mechanism is used to predict shield tunneling-induced settlement with spatiotemporal sequence dependency relationships.Particle swarm optimization and a random forest algorithm are used for optimization and inversion operations in the integrated mechanism,respectively.Subsequent ring position tests showed that the integrated mechanism-based adaptive adjustment strategy limited the average fluctuation of uncontrollable parameters to±13.95%compared to±34.27%for the full optimization strategy.The actual average settlement was only 3.81 mm compared to 4.72 mm for the full optimization strategy through collaborative parameter adjustment.The application validated the feasibility and applicability of the integrated mechanism,providing important references for the adaptive adjustment of shield parameters and tunnel construction automation. 展开更多
关键词 Pre-control of settlement Optimization and inversion Multi-head self-attention(MHSA) Long short-term memory(LSTM) Random forest(RF)algorithm
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Random forest algorithm and regional applications of spectral inversion model for estimating canopy nitrogen concentration in rice 认领 引用 被引量：26
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作者 LI Xuqing LIU Xiangnan LIU Meiling WU Ling 《遥感学报》 CSCD 北大核心 2014年第4期923-945,共23页
Canopy Nitrogen Concentration （CNC） is a key indicator of crop yields. It is feasible to establish a real- time regional model to estimate CNC by upscaling the field-scale spectral model. This study focuses on monit... Canopy Nitrogen Concentration （CNC） is a key indicator of crop yields. It is feasible to establish a real- time regional model to estimate CNC by upscaling the field-scale spectral model. This study focuses on monitoring the CNC in rice on a large scale in real-time. The Random Forest （RF） algorithm is used to establish the CNC spectral inversion model, and some vegetation indexes that are sensitive to nitrogen were selected as input parameters for the RF. CNC was selected as an output parameter. The hyperspectral and biochemical data were collected in a paddy in Changchun City, Jilin Province, China, and the data in Suzhou was used to test the model＇s universality and effectiveness. Two regional-scale models were developed by applying scale transformation based on the input and output variables respectively. The results show that the RFCNC model （CNC spectral inversion model based on the RF algorithm） performed accurately and significantly improved upon existing methods. R2, used to validate method accuracy in Changchun and Suzhou, was 0.82 and 0.73 respectively. The regional application accuracy increased （R2= 0. 81） through the two upscaling methods using hyperspectral remote sensing satellite images. This study suggests that this method is promising for estimating regional CNC in rice by upscaling a field-scale spectral model if the strategy is appropriately selected. 展开更多
关键词 random forest algorithm, nitrogen inversion model, upscaling, regional application
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基于ISSA-RF算法的光伏阵列故障诊断研究 认领 引用
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作者 许桂敏 宋雨航 +2 位作者 相里梦桥 杨亚龙 段晨东 《太阳能学报》 EI CAS CSCD 北大核心 2026年第2期111-121,共11页
提出一种基于改进麻雀搜索(ISSA)优化随机森林(RF)的算法,用以提高光伏阵列故障诊断的准确率。首先,通过搭建光伏阵列模拟5种工况,提取故障向量,构造光伏阵列故障数据集。其次,通过测试函数对灰狼搜索算法(GWO)、粒子群算法(PSO)、ISSA... 提出一种基于改进麻雀搜索(ISSA)优化随机森林(RF)的算法,用以提高光伏阵列故障诊断的准确率。首先,通过搭建光伏阵列模拟5种工况,提取故障向量,构造光伏阵列故障数据集。其次,通过测试函数对灰狼搜索算法(GWO)、粒子群算法(PSO)、ISSA和麻雀搜索算法(SSA)进行寻优对比,发现ISSA在平均值和标准差方面均优于其他算法,显示出更好的鲁棒性。然后,利用光伏阵列故障仿真数据集对ISSA-RF诊断模型进行性能分析,得到ISSA-RF方法整体准确率达到97.06%,比传统RF模型提高6.94个百分点。最后,结合实验室光伏阵列开路、短路、遮荫、老化和正常5种工况数据集对ISSA-RF诊断模型进行验证,证明所提基于ISSA-RF的光伏阵列故障诊断方法具有较高的分类效率和精度,其性能表现优于其他诊断模型。 展开更多
关键词 光伏阵列 故障诊断 改进麻雀搜索算法 随机森林算法
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基于PSO-RF算法的深锥浓密机耙架扭矩预测 认领 引用
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作者 王奕仁 吴爱祥 +3 位作者 阮竹恩 王建栋 杜双成 刘树龙 《中国有色金属学报》 EI CAS CSCD 北大核心 2026年第3期1164-1177,共14页
耙架扭矩是深锥浓密机运行状态的关键评估指标,实现其精准预测将确保设备的安全稳定运行。以某铅锌矿深锥浓密机历史运行数据为样本,通过Spearman和Pearson相关系数矩阵分析控制变量间的线性关联性;基于Kennard-Stone算法对归一化后的... 耙架扭矩是深锥浓密机运行状态的关键评估指标,实现其精准预测将确保设备的安全稳定运行。以某铅锌矿深锥浓密机历史运行数据为样本,通过Spearman和Pearson相关系数矩阵分析控制变量间的线性关联性;基于Kennard-Stone算法对归一化后的数据迭代随机采样,以此构建了5种机器学习算法模型,对比后优选出随机森林(RF),当随机取样量n=20000时,其平均绝对误差EMAE仅0.0102、决定系数R2达0.9801;采用粒子群算法(PSO)优化RF超参数,并在其5折交叉验证过程中重复迭代采样30次训练集,优化后模型的预测精度与泛化能力得到增强,EMAE均值降低至0.0085,R2均值提升至0.9853。PSO-RF算法模型的特征重要性得分显示:底流质量浓度(45.9%)、循环管路质量浓度(25.4%)和泥层高度(11%)是影响耙架扭矩的核心变量。 展开更多
关键词 深锥浓密机 耙架扭矩 粒子群优化算法 随机森林
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基于模态分析和PCA-WOA-RF的磨煤机下架体壳振预测 认领 引用
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作者 赵小惠 刘磊 +3 位作者 蒲军平 成小乐 高畅 胡胜 《山东大学学报（工学版）》 CAS CSCD 北大核心 2026年第1期149-157,168,共9页
为探究磨煤机下架体壳振与其他运行参数之间的复杂非线性映射关系,并提高磨煤机下架体壳振预测的准确性,提出一种基于PCA-WOA-RF模型的磨煤机下架体壳振预测方法。对磨煤机下架体进行模态分析,验证下架体壳振标准值,使用Spearman相关系... 为探究磨煤机下架体壳振与其他运行参数之间的复杂非线性映射关系,并提高磨煤机下架体壳振预测的准确性,提出一种基于PCA-WOA-RF模型的磨煤机下架体壳振预测方法。对磨煤机下架体进行模态分析,验证下架体壳振标准值,使用Spearman相关系数法和主成分分析法(principal component analysis,PCA)对磨煤机工作数据进行相关性分析并提取主成分;以随机森林(random forest,RF)为预测模型结构基础,使用鲸鱼优化算法(whale optimization algorithm,WOA)对模型的超参数进行优化;以国能长源武汉青山热电有限公司磨煤机工作数据进行实例验证,并与PCA-BP、PCA-SVM和PCA-RF模型进行精度对比。结果表明:一次风流量、拉杆应变、磨煤机电机轴振动、中架体壳振、煤量和一次风出入口差压与磨煤机下架体壳振有显著相关性,经过主成分分析法提取的2个主成分方差贡献率达94.569%,所提出的PCA-WOA-RF模型平均预测误差最小,预测精度达到97.80%。该模型进一步提升了磨煤机下架体壳振预测精度。 展开更多
关键词 磨煤机 下架体壳振 主成分分析 随机森林 鲸鱼优化算法
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融合ANP-GWO与RF-TF-IDF的煤矿安全绩效动态评价及预测研究 认领 引用
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作者 刘业娇 李金亮 +4 位作者 闫文杰 姜丰懿 王汇鑫 曹东强 田志超 《矿业安全与环保》 CAS 北大核心 2026年第3期106-117,共12页
针对煤矿安全绩效评价存在的滞后性、指标静态失配等问题,基于人工智能算法搭建了自下而上的煤矿安全绩效动态评估框架。为适配短周期快速评价,在评价指标构建阶段提出前验指标与后验指标交叉融合方法:(1)在建立井工煤矿安全绩效评价指... 针对煤矿安全绩效评价存在的滞后性、指标静态失配等问题,基于人工智能算法搭建了自下而上的煤矿安全绩效动态评估框架。为适配短周期快速评价,在评价指标构建阶段提出前验指标与后验指标交叉融合方法:(1)在建立井工煤矿安全绩效评价指标体系并构建其层次分析网络基础上,引入灰狼优化算法(GWO)融合历史前验指标对安全绩效指标权重进行动态优化;(2)联合采用随机森林(RF)与词频-逆文档频率(TF-IDF)方法挖掘煤矿历史安全绩效相关数据中的隐性风险特征,建立的RF-TF-IDF模型平均曲线下面积值达到0.98;(3)基于长短期记忆网络(LSTM)算法实现短周期安全绩效状态的实时感知与趋势预判。实验结果表明,新模型在测试集上的准确率为82.5%,LSTM预测模型的均方根误差为0.16,能够有效辅助绩效评价与决策。研究成果可进一步提高煤矿安全绩效考核体系的响应速度与预测精度,为煤矿安全绩效的动态评估、及时预警与决策支持提供一种新的智能化工具体系。 展开更多
关键词 煤矿安全绩效 动态评价 网络层次分析法 随机森林 长短期记忆网络 自下而上评价
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基于TSNE-NGO-RF算法的混凝土坝变形预测模型 认领 引用
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作者 郑东健 赵宇 +2 位作者 冉成 林英浩 陈林泽 《郑州大学学报（工学版）》 CAS 北大核心 2026年第2期122-127,135,共6页
对混凝土坝变形监测资料进行合理的数据分析和准确的预测是确保大坝安全长效运行的关键手段,针对影响大坝变形的环境量具有周期性和非线性的特点,以及传统随机森林模型参数寻优方法适用性差和计算效率低等问题,提出了一种新型的大坝变... 对混凝土坝变形监测资料进行合理的数据分析和准确的预测是确保大坝安全长效运行的关键手段,针对影响大坝变形的环境量具有周期性和非线性的特点,以及传统随机森林模型参数寻优方法适用性差和计算效率低等问题,提出了一种新型的大坝变形预测模型。该模型采用t-分布式随机邻域嵌入对特征值进行降维,提高模型的分类性能,并运用北方苍鹰优化算法对传统随机森林模型进行了改进,提高了随机森林模型参数的择优选取效率。运用北方苍鹰优化算法在第80次迭代时即可确定随机森林模型的参数,且适应度函数为0.2493,相较麻雀搜索算法和粒子群优化算法取得了较好的结果。选取某混凝土坝第18#坝段和第26#坝段进行实例分析,结果表明:所提融合模型预测结果的平均绝对误差分别为0.50193和0.17302 mm,均方误差分别为0.35971和0.04387 mm2,平均绝对百分比误差分别为0.81959%,0.11362%,决定系数分别为0.91456和0.89274,相较于其他模型,该模型在预测准确性和模型稳定性方面表现最优,为混凝土坝变形的精准预测开辟了新的可能性。 展开更多
关键词 混凝土坝 变形预测 降维 北方苍鹰优化算法 随机森林算法
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基于KPCA-PSO-RF矿井突水水源判别模型 认领 引用
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作者 刘伟韬 卢润时 杜衍辉 《煤炭科学技术》 EI CAS CSCD 北大核心 2026年第5期378-392,共15页
在复杂地质条件下仅依赖传统水化学特征相似性判别方法难以精准判定矿井水水样来源,因此将机器学习算法与寻优算法相结合,提出了一种基于KPCA-PSO-RF矿井突水水源判别模型。首先在分析主要含水层地下水水化学特征的基础上选取11种水化... 在复杂地质条件下仅依赖传统水化学特征相似性判别方法难以精准判定矿井水水样来源,因此将机器学习算法与寻优算法相结合,提出了一种基于KPCA-PSO-RF矿井突水水源判别模型。首先在分析主要含水层地下水水化学特征的基础上选取11种水化学指标(K++Na+、Ca2+、Mg2+、Cl-、SO42-、HCO3-、CO32-、TDS、TH、TA、pH)作为水源判别特征参数,利用核主成分分析(KPCA)提取3种主要指标作为模型判别的因子,然后通过粒子群优化算法(PSO)对随机森林算法(RF)超参数开展迭代寻优工作,最后将96组地下水样本数据按7∶3划分为训练样本与测试样本进行训练,建立KPCA-PSO-RF模型,并将判别结果与RF、PSO-RF和KPCA-GridSearchCV-RF模型进行对比。结果表明:经5折交叉验证,运用Min-Max标准化方法与KPCA算法降维处理水化学数据并提取前3种主成分,可以有效消除样本间的冗余与重叠,并弥补传统PCA算法无法处理复杂非线性样本的局限性;运用PSO迭代寻优RF超参数,经65次迭代确定最优组合(n_estimators=54、max_depth=11、min_samples_split=7),可以增强模型分类准确性,消除参数设置盲目性;与相关判别模型比较,KPCA-PSO-RF模型在准确率(96.55%)、精确率(97.32%)、召回率(98.21%)、F1分数(0.9659)等方面均优于对比模型,其泛化性更佳,准确性更高。将阳城煤矿10组矿井水水样数据输入到训练好的模型中,判别结果与现场突水实测结果一致,准确判断1308工作面突水水源为奥灰含水层,3305工作面涌水水源为三灰含水层,实现了精准区分。 展开更多
关键词 水源判别 水化学特征 核主成分分析 粒子群优化算法 随机森林算法
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基于WOA-RF模型的航空镍镉电池SOC预测 认领 引用
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作者 雷晓犇 胡新华 王浩 《电子测量与仪器学报》 EI CSCD 北大核心 2026年第1期61-69,共9页
航空镍镉电池的荷电状态(SOC)预测是保障航空器安全运行的关键技术之一,针对传统预测模型精度不足、环境适应性差的问题,提出一种融合鲸鱼优化算法(WOA)与随机森林(RF)的WOA-RF混合预测模型。首先,基于随机森林回归算法构建初始预测模型... 航空镍镉电池的荷电状态(SOC)预测是保障航空器安全运行的关键技术之一,针对传统预测模型精度不足、环境适应性差的问题,提出一种融合鲸鱼优化算法(WOA)与随机森林(RF)的WOA-RF混合预测模型。首先,基于随机森林回归算法构建初始预测模型,利用其多决策树集成优势处理非线性特征;其次,引入鲸鱼优化算法对随机森林的核心超参数(进行全局寻优,解决人工调参效率低下的问题)从而提升模型预测精度与泛化能力。为验证模型性能,在不同温度(20℃、0℃、-10℃、-20℃)环境下分别进行放电循环实验,对比分析WOA-RF与传统RF、反向传播神经网络(BPNN)、支持向量回归(SVR)以及粒子群优化RF(PSO-RF)、遗传算法优化RF(GA-RF)等模型的预测效果。实验结果表明,在标准温度下,WOA-RF模型的平均绝对误差(MAE)为1.22%、决定系数(R2)达到0.986、均方根误差(RMSE)为1.56%,优于对比模型;在低温环境下,WOA-RF的MAE仍保持在1.5%以内,RMSE为1.8%以内,R2高于0.975,表现出更强的环境鲁棒性。结果表明,WOA-RF模型有效提高了SOC预测的准确性和稳定性,尤其适用于航空极端工况下的镍镉电池状态监测,为电池管理系统提供了可靠的技术支持。 展开更多
关键词 航空镍镉电池 SOC预测 鲸鱼优化算法 随机森林
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基于RF-xLSTM模型的结构健康监测数据恢复研究 认领 引用
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作者 杜永峰 王翠云 +1 位作者 朱前坤 郝佳欣 《振动与冲击》 EI CSCD 北大核心 2026年第14期233-244,共12页
针对结构健康监测系统因电源、网络和传感器等原因引起的复杂数据缺失难题,提出一种融合随机森林(random forest,RF)与扩展长短期记忆网络(extended long short-term memory,xLSTM)的双阶段数据恢复模型RF-xLSTM。模型构建了递进式误差... 针对结构健康监测系统因电源、网络和传感器等原因引起的复杂数据缺失难题,提出一种融合随机森林(random forest,RF)与扩展长短期记忆网络(extended long short-term memory,xLSTM)的双阶段数据恢复模型RF-xLSTM。模型构建了递进式误差修正机制:第一阶段利用RF的特征学习能力对缺失值进行初步填充;第二阶段利用xLSTM的深度时序建模优势,捕获数据的非线性时间依赖性并动态修正RF阶段的潜在误差。基于桥梁健康监测系统实测加速度数据,以归一化均方根误差(normalized root mean square error,NRMSE)和平均绝对误差(mean absolute error,MAE)为评估指标,验证了模型在不同数据缺失率(missing rate,MR)下的性能。结果表明,当MR≥30%时,RF-xLSTM恢复精度显著提升,与xLSTM模型相比,NRMSE值可降低2.50%,MAE减少0.006 m/s2;较卷积神经网络模型,NRMSE值降低2.19%,MAE减少0.001 m/s2;较GRU模型,NRMSE值降低3.64%,MAE减少0.004 m/s2;较RF模型,NRMSE值降低2.67%,MAE减少0.002 m/s2。进一步融合邻近传感器的时空相关性,模型在MR≥50%高缺失率下的恢复效果获得实质性提升。算法成功应用于寒冷地区超长隔震结构隔震支座位移数据的缺失恢复,验证了其在复杂结构监测场景下的普适性和有效性。 展开更多
关键词 缺失值恢复 随机森林-扩展长短期记忆网络(RF-xLSTM) 数据缺失率 结构健康监测(SHM)
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