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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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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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Multi-state Information Dimension Reduction Based on Particle Swarm Optimization-Kernel Independent Component Analysis 认领 引用
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作者 邓士杰 苏续军 +1 位作者 唐力伟 张英波 《Journal of Donghua University(English Edition)》 CAS 2017年第6期791-795,共5页
The precision of the kernel independent component analysis( KICA) algorithm depends on the type and parameter values of kernel function. Therefore,it's of great significance to study the choice method of KICA'... The precision of the kernel independent component analysis( KICA) algorithm depends on the type and parameter values of kernel function. Therefore,it's of great significance to study the choice method of KICA's kernel parameters for improving its feature dimension reduction result. In this paper, a fitness function was established by use of the ideal of Fisher discrimination function firstly. Then the global optimal solution of fitness function was searched by particle swarm optimization( PSO) algorithm and a multi-state information dimension reduction algorithm based on PSO-KICA was established. Finally,the validity of this algorithm to enhance the precision of feature dimension reduction has been proven. 展开更多
关键词 kernel independent component analysis(KICA) particle swarm optimization(PSO) feature dimension reduction fitness function
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Comparison of Kernel Entropy Component Analysis with Several Dimensionality Reduction Methods 认领 引用
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作者 马西沛 张蕾 孙以泽 《Journal of Donghua University(English Edition)》 CAS 2017年第4期577-582,共6页
Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducte... Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducted a comparative study of KECA with other five dimensionality reduction methods,principal component analysis( PCA),kernel PCA( KPCA),locally linear embedding( LLE),laplacian eigenmaps( LAE) and diffusion maps( DM). Three quality assessment criteria, local continuity meta-criterion( LCMC),trustworthiness and continuity measure(T&C),and mean relative rank error( MRRE) are applied as direct performance indexes to assess those dimensionality reduction methods. Moreover,the clustering accuracy is used as an indirect performance index to evaluate the quality of the representative data gotten by those methods. The comparisons are performed on six datasets and the results are analyzed by Friedman test with the corresponding post-hoc tests. The results indicate that KECA shows an excellent performance in both quality assessment criteria and clustering accuracy assessing. 展开更多
关键词 dimensionality reduction kernel entropy component analysis(KECA) kernel principal component analysis(KPCA) clustering
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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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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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Two linear subpattern dimensionality reduction algorithms 认领 引用 被引量：3
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作者 贲晛烨 孟维晓 +1 位作者 王泽 王科俊 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2012年第5期47-53,共7页
This paper presents two novel algorithms for feature extraction-Subpattern Complete Two Dimensional Linear Discriminant Principal Component Analysis (SpC2DLDPCA) and Subpattern Complete Two Dimensional Locality Preser... This paper presents two novel algorithms for feature extraction-Subpattern Complete Two Dimensional Linear Discriminant Principal Component Analysis (SpC2DLDPCA) and Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA). The modified SpC2DLDPCA and SpC2DLPPCA algorithm over their non-subpattern version and Subpattern Complete Two Dimensional Principal Component Analysis (SpC2DPCA) methods benefit greatly in the following four points: (1) SpC2DLDPCA and SpC2DLPPCA can avoid the failure that the larger dimension matrix may bring about more consuming time on computing their eigenvalues and eigenvectors. (2) SpC2DLDPCA and SpC2DLPPCA can extract local information to implement recognition. (3)The idea of subblock is introduced into Two Dimensional Principal Component Analysis (2DPCA) and Two Dimensional Linear Discriminant Analysis (2DLDA). SpC2DLDPCA combines a discriminant analysis and a compression technique with low energy loss. (4) The idea is also introduced into 2DPCA and Two Dimensional Locality Preserving projections (2DLPP), so SpC2DLPPCA can preserve local neighbor graph structure and compact feature expressions. Finally, the experiments on the CASIA(B) gait database show that SpC2DLDPCA and SpC2DLPPCA have higher recognition accuracies than their non-subpattern versions and SpC2DPCA. 展开更多
关键词 subpattern dimensionality reduction Subpattern Complete Two Dimensional Linear Discriminant Principal Component Analysis (SpC2DLDPCA) Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA) gait recognition
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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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Description and Classification of Leather Defects Based on Principal Component Analysis 认领 引用
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作者 DING Caihong HUANG Hao YANG Yanzhu 《Journal of Donghua University(English Edition)》 CAS 2018年第6期473-479,共7页
The accurate extraction and classification of leather defects is an important guarantee for the automation and quality evaluation of leather industry. Aiming at the problem of data classification of leather defects,a ... The accurate extraction and classification of leather defects is an important guarantee for the automation and quality evaluation of leather industry. Aiming at the problem of data classification of leather defects,a hierarchical classification for defects is proposed.Firstly,samples are collected according to the method of minimum rectangle,and defects are extracted by image processing method.According to the geometric features of representation, they are divided into dot,line and surface for rough classification. From analysing the data which extracting the defects of geometry,gray and texture,the dominating characteristics can be acquired. Each type of defect by choosing different and representative characteristics,reducing the dimension of the data,and through these characteristics of clustering to achieve convergence effectively,realize extracted accurately,and digitized the defect characteristics,eventually establish the database. The results showthat this method can achieve more than 90% accuracy and greatly improve the accuracy of classification. 展开更多
关键词 defect detection hierarchical classification principal component analysis reduce dimension clustering model
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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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Using Decision Tree Classification and Principal Component Analysis to Predict Ethnicity Based on Individual Characteristics: A Case Study of Assam and Bhutan Ethnicities 认领 引用
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作者 Tianhui Zhang Xinyu Zhang +2 位作者 Xianchen Liu Zhen Guo Yuanhao Tian 《Journal of Software Engineering and Applications》 2024年第12期833-850,共18页
This study investigates the use of a decision tree classification model, combined with Principal Component Analysis (PCA), to distinguish between Assam and Bhutan ethnic groups based on specific anthropometric feature... This study investigates the use of a decision tree classification model, combined with Principal Component Analysis (PCA), to distinguish between Assam and Bhutan ethnic groups based on specific anthropometric features, including age, height, tail length, hair length, bang length, reach, and earlobe type. The dataset was reduced using PCA, which identified height, reach, and age as key features contributing to variance. However, while PCA effectively reduced dimensionality, it faced challenges in clearly distinguishing between the two ethnic groups, a limitation noted in previous research. In contrast, the decision tree model performed significantly better, establishing clear decision boundaries and achieving high classification accuracy. The decision tree consistently selected Height and Reach as the most important classifiers, a finding supported by existing studies on ethnic differences in Northeast India. The results highlight the strengths of combining PCA for dimensionality reduction with decision tree models for classification tasks. While PCA alone was insufficient for optimal class separation, its integration with decision trees improved both the model’s accuracy and interpretability. Future research could explore other machine learning models to enhance classification and examine a broader set of anthropometric features for more comprehensive ethnic group classification. 展开更多
关键词 Decision Tree Classification Principal Component Analysis Anthropometric Features Dimensionality Reduction Machine Learning in Anthropology
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Model-based Predictive Control for Spatially-distributed Systems Using Dimensional Reduction Models 认领 引用 被引量：4
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作者 Meng-Ling Wang Ning Li Shao-Yuan Li 《International Journal of Automation and computing》 2011年第1期1-7,共7页
In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems ... In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems （SDSs）. First, the dimension reduction with principal component analysis （PCA） is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies. 展开更多
关键词 Spatially-distributed system principal component analysis （PCA） time/space separation dimension reduction model predictive control （MPC）.
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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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Optimizing progress variable definition in flamelet-based dimension reduction in combustion 认领 引用 被引量：3
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作者 Jing CHEN Minghou LIU Yiliang CHEN 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2015年第11期1481-1498,共18页
An automated method to optimize the definition of the progress variables in the flamelet-based dimension reduction is proposed. The performance of these optimized progress variables in coupling the flamelets and flow ... An automated method to optimize the definition of the progress variables in the flamelet-based dimension reduction is proposed. The performance of these optimized progress variables in coupling the flamelets and flow solver is presented. In the proposed method, the progress variables are defined according to the first two principal components （PCs） from the principal component analysis （PCA） or kernel-density-weighted PCA （KEDPCA） of a set of flamelets. These flamelets can then be mapped to these new progress variables instead of the mixture fraction/conventional progress variables. Thus, a new chemistry look-up table is constructed. A priori validation of these optimized progress variables and the new chemistry table is implemented in a CH4/N2/air lift-off flame. The reconstruction of the lift-off flame shows that the optimized progress variables perform better than the conventional ones, especially in the high temperature area. The coefficient determinations （R2 statistics） show that the KEDPCA performs slightly better than the PCA except for some minor species. The main advantage of the KEDPCA is that it is less sensitive to the database. Meanwhile, the criteria for the optimization are proposed and discussed. The constraint that the progress variables should monotonically evolve from fresh gas to burnt gas is analyzed in detail. 展开更多
关键词 principal component analysis （PCA）,oprogress variable flamelet-basedmodel dimension reduction
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致密砂岩储层孔隙结构对流体可动程度的影响及综合表征 认领 引用 被引量：2
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作者 陈琪泉 李超 +5 位作者 成友友 谭习群 罗翔 赵子萱 闫晨辉 谭成仟 《地质科技通报》 CAS CSCD 北大核心 2026年第2期91-105,共15页
储层流体可动程度受储层物性及孔隙结构的综合影响显著,单一参数无法准确表征。为了明确孔隙结构综合表征在流体可动程度评价中的有效性,针对鄂尔多斯盆地合水地区三叠系延长组长82致密砂岩储层,旨在探究孔隙结构参数与流体可动程度... 储层流体可动程度受储层物性及孔隙结构的综合影响显著,单一参数无法准确表征。为了明确孔隙结构综合表征在流体可动程度评价中的有效性,针对鄂尔多斯盆地合水地区三叠系延长组长82致密砂岩储层,旨在探究孔隙结构参数与流体可动程度的关系。采用核磁共振(NMR)、高压压汞(HPMI)联测的实验方法,计算有效可动流体孔隙度(φcutoff)及有效可动流体孔喉半径(rcutoff),并结合分形理论分析孔隙结构。进而使用主成分分析法(PCA)提取因子,并引入k-means聚类分析法,建立了致密砂岩流体可动程度综合评价方法。结果表明:研究区目的层孔喉类型以中小孔—中细喉为主,可动流体主要赋存在孔喉半径0.01~1.0μm的孔隙中;分形维数介于2.440 0~2.741 2之间,平均值2.55;有效可动流体孔喉半径介于0.016~0.095μm之间,平均值为0.049μm;有效可动流体孔隙度介于0.716%~2.980%之间,平均值为1.598%。综合流体可动程度参数、物性参数、孔隙结构参数,提取4个主成分,分别评估储层渗流能力与储集能力、微观非均质性、产出能力、大孔喉占比。基于前3个主成分输出聚类结果,将实验样品划分为Ⅰ类、Ⅱ类、Ⅲ类。流体可动程度的综合评价结果可为同类型致密砂岩油藏储层评价及高效开发提供一定依据。 展开更多
关键词 联合表征 孔隙结构 分形维数 主成分分析(PCA) k-means聚类分析 流体可动程度
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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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基于主成分分析和Transformer模型的离心泵耦合故障智能诊断方法 认领 引用 被引量：1
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作者 姜大连 许文 +3 位作者 吴尉 陈涛 周许杰 董亮 《机电工程》 CAS 北大核心 2026年第3期627-636,共10页
针对离心泵单一故障、耦合故障及不同程度故障在诊断过程中存在模式识别难度大、特征提取复杂度高及数据非线性可分等问题,提出了一种基于主成分分析(PCA)与Transformer模型的智能故障诊断方法。首先,对多源异构传感器采集的原始振动信... 针对离心泵单一故障、耦合故障及不同程度故障在诊断过程中存在模式识别难度大、特征提取复杂度高及数据非线性可分等问题,提出了一种基于主成分分析(PCA)与Transformer模型的智能故障诊断方法。首先,对多源异构传感器采集的原始振动信号进行了归一化预处理,以消除量纲差异并提升数据可靠性;然后,采用PCA对数据进行了降维处理,有效剔除了冗余信息,保留了最具判别性的关键特征;接着,完成预处理后,根据数据规模调整了Transformer模型中注意力机制头数等参数,并在优化后的网络结构上对PCA-Transformer模型进行了深度学习训练;最后,采用实验验证了PCA-Transformer模型在故障识别中的有效性。研究结果表明:预处理后的模型诊断准确率提升至99%,较原始数据提高了3%;训练准确率提升了2.5%,训练损失降低了75%。该方法在离心泵的单一故障、耦合故障及不同发展阶段的识别中均表现出良好性能,可为设备的智能监测与安全运行提供有力支撑。 展开更多
关键词 离心泵 故障诊断模型 主成分分析 Transformer理论 注意力机制 数据降维处理 智能监测
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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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