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Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means 认领 引用 被引量：4
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作者 Bo-Han Wu Ran-Hong Xie +3 位作者 Li-Zhi Xiao Jiang-Feng Guo Guo-Wen Jin Jian-Wei Fu 《Petroleum Science》 SCIE EI CAS CSCD 2023年第5期2747-2758,共12页
In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tig... In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method. 展开更多
关键词 Tight sandstone Integrated reservoir classification Principal component analysis Simulated annealing genetic algorithm Fuzzy cluster means
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Aerodynamic multi-objective integrated optimization based on principal component analysis 认领 引用 被引量：17
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作者 Jiangtao HUANG Zhu ZHOU +2 位作者 Zhenghong GAO Miao ZHANG Lei YU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2017年第4期1336-1348,共13页
Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which,... Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency,the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil,and the proposed method is integrated into aircraft multi-disciplinary design（AMDEsign） platform, which contains aerodynamics, stealth and structure weight analysis and optimization module.Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem. 展开更多
关键词 Aerodynamic optimization Dimensional reduction Improved multi-objective particle swarm optimization（MOPSO） algorithm Multi-objective Principal component analysis
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Polarimetric Meteorological Satellite Data Processing Software Classification Based on Principal Component Analysis and Improved K-Means Algorithm 认领 引用 被引量：2
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作者 Manyun Lin Xiangang Zhao +3 位作者 Cunqun Fan Lizi Xie Lan Wei Peng Guo 《Journal of Geoscience and Environment Protection》 2017年第7期39-48,共10页
With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In th... With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Firstly, principal component analysis (PCA) is used to reduce the dimension of software running feature data and to interpret software characteristic information. Then the modified K-means algorithm was used to classify the meteorological data processing software. Finally, it combined with the results of principal component analysis to explain the significance of various types of integrated software operating characteristics. And it is used as the basis for optimizing the allocation of software hardware resources and improving the efficiency of software operation. 展开更多
关键词 Principal Component Analysis Improved K-Mean Algorithm Meteorological Data Processing Feature Analysis Similarity Algorithm
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Support vector classifier based on principal component analysis 认领 引用 被引量：2
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作者 Zheng Chunhong Jiao Licheng Li Yongzhao 《Journal of Systems Engineering and Electronics》 SCIE EI 2008年第1期184-190,共7页
Support vector classifier(SVC)has the superior advantages for small sample learning problems with high dimensions,with especially better generalization ability.However there is some redundancy among the high dimension... Support vector classifier(SVC)has the superior advantages for small sample learning problems with high dimensions,with especially better generalization ability.However there is some redundancy among the high dimensions of the original samples and the main features of the samples may be picked up first to improve the performance of SVC.A principal component analysis(PCA)is employed to reduce the feature dimensions of the original samples and the pre-selected main features efficiently,and an SVC is constructed in the selected feature space to improve the learning speed and identification rate of SVC.Furthermore,a heuristic genetic algorithm-based automatic model selection is proposed to determine the hyperparameters of SVC to evaluate the performance of the learning machines.Experiments performed on the Heart and Adult benchmark data sets demonstrate that the proposed PCA-based SVC not only reduces the test time drastically,but also improves the identify rates effectively. 展开更多
关键词 support vector classifier principal component analysis feature selection genetic algorithms
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Improved Face Recognition Method Using Genetic Principal Component Analysis 认领 引用 被引量：2
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作者 E.Gomathi K.Baskaran 《Journal of Electronic Science and Technology》 CAS 2010年第4期372-378,共7页
An improved face recognition method is proposed based on principal component analysis （PCA） compounded with genetic algorithm （GA）, named as genetic based principal component analysis （GPCA）. Initially the eigen... An improved face recognition method is proposed based on principal component analysis （PCA） compounded with genetic algorithm （GA）, named as genetic based principal component analysis （GPCA）. Initially the eigenspace is created with eigenvalues and eigenvectors. From this space, the eigenfaces are constructed, and the most relevant eigenfaees have been selected using GPCA. With these eigenfaees, the input images are classified based on Euclidian distance. The proposed method was tested on ORL （Olivetti Research Labs） face database. Experimental results on this database demonstrate that the effectiveness of the proposed method for face recognition has less misclassification in comparison with previous methods. 展开更多
关键词 Eigenfaces eigenvectors face recognition genetic algorithm principal component analysis.
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Generalized two-dimensional correlation near-infrared spectroscopy and principal component analysis of the structures of methanol and ethanol 认领 引用 被引量：7
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作者 Liu Hao Xu JianPing +1 位作者 Qu LingBo Xiang BingRen 《Science China Chemistry》 SCIE EI CAS 2010年第5期1154-1159,共6页
Liquid state methanol and ethanol under different temperatures have been investigated by FT-NIR(Fourier transform nearinfrared) spectroscopy,generalized two-dimensional(2D) correlation spectroscopy,and PCA(principal c... Liquid state methanol and ethanol under different temperatures have been investigated by FT-NIR(Fourier transform nearinfrared) spectroscopy,generalized two-dimensional(2D) correlation spectroscopy,and PCA(principal component analysis) . First,the FT-NIR spectra were measured over a temperature range of 30-64(or 30-71) °C,and then the 2D correlation spectra were computed.Combining near-infrared spectroscopy,generalized 2D correlation spectroscopy,and references,we analyzed the molecular structures(especially the hydrogen bond) of methanol and ethanol,and performed the NIR band assignments. The PCA method was employed to verify the results of the 2D analysis.This study will be helpful to the understanding of these reagents. 展开更多
关键词 NIR(near-infrared) two-dimensional (2D) correlation spectroscopy principal component analysis (PCA) methanol ethanol
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Kernel Factor Analysis Algorithm with Varimax 认领 引用
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作者 夏国恩 金炜东 张葛祥 《Journal of Southwest Jiaotong University(English Edition)》 2006年第4期394-399,共6页
Kernal factor analysis （KFA） with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle com... Kernal factor analysis （KFA） with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle component analysis （KPCA）. The results show that the best error rate in handwritten digit recognition by kernel factor analysis with vadmax （4.2%） was superior to KPCA （4.4%）. The KFA with varimax could more accurately image handwritten digit recognition. 展开更多
关键词 Kernel factor analysis Kernel principal component analysis Support vector machine Varimax Algorithm Handwritten digit recognition
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Convergence of algorithms used for principal component analysis 认领 引用 被引量：1
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作者 张俊华 陈翰馥 《Science China(Technological Sciences)》 EI CAS 1997年第6期597-604,共8页
The convergence of algorithms used for principal component analysis is analyzed. The algorithms are proved to converge to eigenvectors and eigenvalues of a matrix A which is the expectation of observed random samples.... The convergence of algorithms used for principal component analysis is analyzed. The algorithms are proved to converge to eigenvectors and eigenvalues of a matrix A which is the expectation of observed random samples. The conditions required here are considerably weaker than those used in previous work. 展开更多
关键词 principal component analysis stochastic approximation algorithms convergence.
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Hybrid adaptive machine learning approach for detection and mitigation of GNSS spoofing through enhanced osprey optimization algorithm 认领 引用
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作者 KOTI Sushmitha SANDHYA Rachamalla 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期1059-1080,共22页
Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,veloci... Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,velocity,and time(PVT).Several GNSS devices are exposed to detect spoofing attacks due to the use of unsafe locations.In addition,misleading signals are intentionally used to generate timing and position,and GNSS signal spoofing provides a constant risk to consumers.In past works,the implementation of the Global Positioning System(GPS)in autonomous vehicle navigation might be endangered by spoofing.To mitigate these issues,this task develops a hybrid machine-learning method for mitigating and detecting GNSS spoofing attacks.The developed model is processed with three phases:data collection,feature extraction,and detection.Initially,the required data is taken from the standard resource.Then,the data is given to the feature extraction phase.The features of the data are retrieved using the principal component analysis(PCA)and t-distributed stochastic neighbor embedding(t-SNE)model.The features obtained from the collected data are transferred to the detection phase.In the final phase,the GNSS spoofing detection and mitigation is executed using a machine learning method called as hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP).Enhanced osprey optimization algorithm(EOOA)is utilized for optimizing the variables to enhance the efficacy of models and achieves greater performance than other standard models. 展开更多
关键词 Global Navigation Satellite System(GNSS) detection and mitigation of GNSS t-distributed stochastic neighbor embedding(t-SNE) enhanced osprey optimization algorithm principal component analysis(PCA) hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP)
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Coal and gas outburst prediction model based on principal component analysis and improved support vector machine 认领 引用 被引量：6
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作者 Chaojun Fan Xinfeng Lai +1 位作者 Haiou Wen Lei Yang 《Geohazard Mechanics》 2023年第4期319-324,共6页
In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was designed.Principal component analysis(PCA)was used to pre-process the original data ... In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was designed.Principal component analysis(PCA)was used to pre-process the original data samples,extract the principal components of the samples,use firefly algorithm(FA)to improve the support vector machine model,and compare and analyze the prediction results of PCA-FA-SVM model with BP model,FA-SVM model,FA-BP model and SVM model.Accuracy rate,recall rate,Macro-F1 and model prediction time were used as evaluation indexes.The results show that:Principal component analysis improves the prediction efficiency and accuracy of FA-SVM model.The accuracy rate of PCA-FA-SVM model predicting coal and gas outburst risk is 0.962,recall rate is 0.955,Macro-F1 is 0.957,and model prediction time is 0.312s.Compared with other models,The comprehensive performance of PCA-FA-SVM model is better. 展开更多
关键词 Coal and gas outburst Risk prediction Principal component analysis(PCA) Firefly algorithm(FA) Support vector machine(SVM)
Prediction of coal and gas outburst hazard using kernel principal component analysis and an enhanced extreme learning machine approach 认领 引用 被引量：2
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作者 Kailong Xue Yun Qi +2 位作者 Hongfei Duan Anye Cao Aiwen Wang 《Geohazard Mechanics》 2024年第4期279-288,共10页
In order to enhance the accuracy and efficiency of coal and gas outburst prediction,a novel approach combining Kernel Principal Component Analysis(KPCA)with an Improved Whale Optimization Algorithm(IWOA)optimized extr... In order to enhance the accuracy and efficiency of coal and gas outburst prediction,a novel approach combining Kernel Principal Component Analysis(KPCA)with an Improved Whale Optimization Algorithm(IWOA)optimized extreme learning machine(ELM)is proposed for precise forecasting of coal and gas outburst disasters in mines.Firstly,based on the influencing factors of coal and gas outburst disasters,nine coupling indexes are selected,including gas pressure,geological structure,initial velocity of gas emission,and coal structure type.The correlation between each index was analyzed using the Pearson correlation coefficient matrix in SPSS 27,followed by extraction of the principal components of the original data through Kernel Principal Component Analysis(KPCA).The Whale Optimization Algorithm(WOA)was enhanced by incorporating adaptive weight,variable helix position update,and optimal neighborhood disturbance to augment its performance.The improved Whale Optimization Algorithm(IWOA)is subsequently employed to optimize the weight Φ of the Extreme Learning Machine(ELM)input layer and the threshold g of the hidden layer,thereby enhancing its predictive accuracy and mitigating the issue of"over-fitting"associated with ELM to some extent.The principal components extracted by KPCA were utilized as input,while the outburst risk grade served as output.Subsequently,a comparative analysis was conducted between these results and those obtained from WOA-SVC,PSO-BPNN,and SSA-RF models.The IWOA-ELM model accurately predicts the risk grade of coal and gas outburst disasters,with results consistent with actual situations.Compared to other models tested,the model's performance showed an increase in Ac by 0.2,0.3,and 0.2 respectively;P increased by 0.15,0.2167,and 0.1333 respectively;R increased by 0.25,0.3,and 0.2333 respectively;F1-Score increased by 0.2031,0.2607,and 0.1864 respectively;Kappa coefficient k increased by 0.3226,0.4762 and 0.3175,respectively.The practicality and stability of the IWOAELM model were verified through its application in a coal mine in Shanxi Province where the predicted values exactly matched the actual values.This indicates that this model is more suitable for predicting coal and gas outburst disaster risks. 展开更多
关键词 Coal and gas outburst Risk prediction Kernel principal component analysis(KPCA) Improved whale optimization algorithm(IWOA) Extreme learning machine(ELM)
Research on Application of Enhanced Neural Networks in Software Risk Analysis 认领 引用
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作者 Zhenbang Rong Juhua Chen +1 位作者 Mei Liu Yong Hu 《南昌工程学院学报》 CAS 2006年第2期112-116,121,共5页
This paper puts forward a risk analysis model for software projects using enranced neural networks.The data for analysis are acquired through questionnaires from real software projects. To solve the multicollinearity ... This paper puts forward a risk analysis model for software projects using enranced neural networks.The data for analysis are acquired through questionnaires from real software projects. To solve the multicollinearity in software risks, the method of principal components analysis is adopted in the model to enhance network stability.To solve uncertainty of the neural networks structure and the uncertainty of the initial weights, genetic algorithms is employed.The experimental result reveals that the precision of software risk analysis can be improved by using the erhanced neural networks model. 展开更多
关键词 software risk analysis principal components analysis back propagation neural networks genetic algorithms
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基于PCA-LDA-CatBoost的煤与瓦斯突出预测 认领 引用 被引量：3
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作者 阎馨 卓志远 屠乃威 《控制工程》 CSCD 北大核心 2026年第4期757-763,共7页
为了对煤与瓦斯突出进行快速准确的预测,基于主成分分析(principal component analysis,PCA)、线性判别分析(linear discriminant analysis,LDA)和分类提升(categorical boosting,CatBoost)算法构建了PCA-LDA-CatBoost模型。首先,将PCA... 为了对煤与瓦斯突出进行快速准确的预测,基于主成分分析(principal component analysis,PCA)、线性判别分析(linear discriminant analysis,LDA)和分类提升(categorical boosting,CatBoost)算法构建了PCA-LDA-CatBoost模型。首先,将PCA与LDA相结合,提出PCA-LDA融合方法对数据进行降维处理,避免损失数据中的有效信息;然后,将降维后的数据输入CatBoost模型中,训练模型的结构和关键参数。实验结果表明,PCA-LDA融合方法可以有效地降低数据的维度,减少噪声和低频类别数据对数据分布的影响;相较于支持向量机模型、BP神经网络模型和朴素贝叶斯模型,PCA-LDA-CatBoost模型能够实现小规模样本下的训练和高精度预测,提高了煤与瓦斯突出预测性能,可以为煤矿开采工作提供参考,实现安全生产。 展开更多
关键词 煤与瓦斯突出预测 CatBoost算法 主成分分析 线性判别分析
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Cooperative Metaheuristics with Dynamic Dimension Reduction for High-Dimensional Optimization Problems 认领 引用
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作者 Junxiang Li Zhipeng Dong +2 位作者 Ben Han Jianqiao Chen Xinxin Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第1期1484-1502,共19页
Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when ta... Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems. 展开更多
关键词 Dimension reduction modified principal components analysis high-dimensional optimization problems cooperative metaheuristics metaheuristic algorithms
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基于多源指标降维和WaoA-ELM模型的锂离子电池健康状态估计 认领 引用 被引量：3
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作者 杨锡运 柏永华 +1 位作者 李艳军 马文兵 《华北电力大学学报（自然科学版）》 CAS 北大核心 2026年第3期88-98,共11页
准确的锂离子电池健康状态(State of Health,SOH)估计是电池管理系统安全稳定运行的重要保障。针对当前电池健康状态估计中存在的特征提取繁琐,估计模型复杂等问题,提出了一种基于多源指标降维和海象优化算法(Walrus Optimization Algor... 准确的锂离子电池健康状态(State of Health,SOH)估计是电池管理系统安全稳定运行的重要保障。针对当前电池健康状态估计中存在的特征提取繁琐,估计模型复杂等问题,提出了一种基于多源指标降维和海象优化算法(Walrus Optimization Algorithm,WaOA)优化极限学习机(Extreme Learning Machine,ELM)的轻量级电池健康状态估计方法。首先从电池的充电和放电两个阶段的电压、电流和温度变化曲线中直接提取潜在特征,选取与SOH高度相关的特征。然后引入方差膨胀因子进行冗余分析,利用主成分分析法有效减少特征冗余。其次采用WaOA优化ELM的超参数,建立WaOA-ELM模型。最后根据NASA公开数据集将所提方法与LSTM、 SVR等模型进行对比。结果表明,所提方法能够准确地估计锂离子电池的健康状态,平均绝对误差(MAE)和均方根误差(RMSE)均在1%以内。 展开更多
关键词 锂离子电池 健康状态 海象优化算法 极限学习机 方差膨胀因子 主成分分析
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基于NSGA-Ⅱ参数辨识算法的分布式光伏动态分群等值建模方法研究 认领 引用
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作者 熊雪君 张雅君 +3 位作者 杨心刚 潘爱强 杨秀 李文豪 《电测与仪表》 CSCD 北大核心 2026年第4期1-10,共10页
随着分布式光伏渗透率不断提高,传统的光伏建模方法已无法满足建模需求。为建立大规模地区分布式的光伏系统的模型并研究其运行特性,提出一种分布式光伏发电系统动态分群多机等值建模方法。文中选择具有代表性的故障曲线的特征点作为聚... 随着分布式光伏渗透率不断提高,传统的光伏建模方法已无法满足建模需求。为建立大规模地区分布式的光伏系统的模型并研究其运行特性,提出一种分布式光伏发电系统动态分群多机等值建模方法。文中选择具有代表性的故障曲线的特征点作为聚类指标,然后根据改进的FCM(fuzzy-C means)聚类算法以选取的特征点为指标对区域内的分布式光伏系统进行聚类,然后对等值模型进行参数设计,对于难以获得的控制参数,提出一种基于非支配性排序遗传算法Ⅱ(non-dominated sorting genetic algorithm Ⅱ,NSGA-Ⅱ)和主成分分析的多目标优化算法来对控制参数进行辨识,其他参数根据容量加权法获得。从而构建区域分布式光伏系统多机等值模型。通过Simulink仿真工具验证了所提等值建模方案能够明显地提高分布式光伏等值模型的准确性。 展开更多
关键词 光伏发电 聚类 分群等值建模 改进的FCM算法 NSGA-Ⅱ 主成分分析
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黑龙江省光伏气候的主成分与聚类分析 认领 引用
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作者 王峙文 杨大智 +2 位作者 刘佰 邱海芝 邵珠航 《太阳能学报》 EI CAS CSCD 北大核心 2026年第7期564-570,共7页
提出一种基于主成分分析和聚类算法的光伏资源评级方法。首先获取2016—2020年基于日本葵花8号卫星反演的黑龙江省的太阳辐照度及次级气象要素(空间分辨率4 km),其次通过时间序列分析方法提取5个要素的9种时间特征并使用主成分分析法对... 提出一种基于主成分分析和聚类算法的光伏资源评级方法。首先获取2016—2020年基于日本葵花8号卫星反演的黑龙江省的太阳辐照度及次级气象要素(空间分辨率4 km),其次通过时间序列分析方法提取5个要素的9种时间特征并使用主成分分析法对所提取特征进行降维,然后通过聚类算法,绘制黑龙江省电站尺度的光伏气候空间分布图谱。该图谱将黑龙江省太阳能资源总量评级为一般、较丰富和很丰富3个区域,可为后续精细化光伏资源评估提供重要科学支撑。 展开更多
关键词 光伏 主成分分析法 聚类算法 资源评估 黑龙江省 太阳能
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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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基于机器学习的水稻始穗期预测方法 认领 引用
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作者 任义方 朱凤 陈思宁 《中国农业气象》 CSCD 2026年第4期558-571,共14页
水稻始穗期是稻曲病、穗稻瘟、纹枯病等病害病菌侵入谷粒等器官造成稻米品质降低产量减少的关键期。为提高水稻病害防治针对性,更好满足“预防为主,综合防治,绿色控害,减药增效”的要求,本文以江苏单季稻为例,利用历史气象资料和水稻生... 水稻始穗期是稻曲病、穗稻瘟、纹枯病等病害病菌侵入谷粒等器官造成稻米品质降低产量减少的关键期。为提高水稻病害防治针对性,更好满足“预防为主,综合防治,绿色控害,减药增效”的要求,本文以江苏单季稻为例,利用历史气象资料和水稻生育期观测资料,在分析水稻始穗期特征及其关键影响因子基础上,应用主成分分析法(PCA)、误差反向传播神经网络算法(BP)、随机森林算法(RF)研究水稻始穗期的预估方法。设置4组模拟方案分别建立水稻始穗期预测模型,以决定系数、均方根误差作为评判指标,对模型精度及其普适性进行分析评价。结果表明:苏北、苏中、苏南地区水稻始穗期的跨度分别在8月4−31日、8月9日−9月18日和8月16日−9月20日,各区平均标准差分别为4d、6d和5d;江苏各区影响水稻始穗期的关键因子基本一致,水稻始穗前3个生育期日序最为关键,播种−分蘖、分蘖−拔节、拔节−孕穗三个生育阶段的温度类因子重要性明显大于降水和日照类因子;与基于RF算法模型相比,基于BP算法的模型模拟精度更高,且对PCA处理后消除相关性的预测因子具有更好的“接纳性”,对江苏各区水稻始穗期模拟预测误差均在2d以内,预测提前量在10d左右,可为准确把握水稻病害防治关键期提供技术支撑。 展开更多
关键词 水稻 始穗期 主成分分析 随机森林算法 误差反向传播神经网络算法
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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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