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Gravitational Dynamic Radius Nearest Neighbor Trained by Fuzzy Enhanced Hiking Optimization Algorithm for Imbalanced Data Classification 认领 引用
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作者 Zahra Beheshti 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1826-1848,共23页
Imbalanced data classification is one of the most critical challenges in machine learning.Standard classifiers do not provide adequate accuracy for detecting minority samples because these classifiers are biased towar... Imbalanced data classification is one of the most critical challenges in machine learning.Standard classifiers do not provide adequate accuracy for detecting minority samples because these classifiers are biased toward the majority samples.To overcome this drawback,many methods have been proposed.One prominent method is the Gravitational Fixed Radius Nearest Neighbor(GFRNN)algorithm,which applies Newton’s law of universal gravitation to determine the class of a test sample based on two parameters:mass and radius.Although GFRNN shows good performance on some imbalanced datasets,it faces several fundamental problems,including ignoring the data distribution and the improper calculation of radius and mass.In this study,a Gravitational Dynamic Radius Nearest Neighbor trained by a Fuzzy Enhanced Hiking Optimization Algorithm(FEHOA-GDRNN)is proposed to improve GFRNN performance.In FEHOA-GDRNN,Enhanced Hiking Optimization Algorithm(EHOA)applies a new spider web search to find better solution based on a Mamdani Fuzzy Inference System(FIS).FEHOA-GDRNN is evaluated on 40 imbalanced datasets and its results are compared with GDRNN trained by Fuzzy HOA(FHOA-GDRNN),GFRNN trained by HOA(HOA-GFRNN),GFRNN and its various versions(IGFRNN,I-GFRNN,and EGDRNN),Cost-Sensitive Support Vector Machine with an RBF kernel(CS-SVM-RBF),Cost-Sensitive Support Vector Machine with a Linear kernel(CS-SVM-Linear),Cost-Sensitive Naïve Bayes(CS-NB),Binary Decision tree(BDT),Random Forest(RF),Gaussian-Probabilistic Neural Network(GaussianPNN)and Skew-Probabilistic Neural Network(SkewPNN).Moreover,the results of the proposed classifier are compared with those of several Fuzzy K-Nearest Neighbor(FKNN).The results demonstrate that FEHOA-GDRNN outperforms other methods in key metrics,including Average Accuracy(AAcc)and Geometric Mean(GM). 展开更多
关键词 Imbalanced data Classification Gravitational Fixed Radius Nearest Neighbor(GFRNN) Mamdani Fuzzy Inference System Hiking Optimization Algorithm(HOA)
Research Review of Deep Learning Algorithms for Agricultural Disease Image Classification 认领 引用
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作者 Shengjiu JIANG Qian WANG 《Plant Diseases and Pests》 2026年第1期30-34,共5页
In the context of rural revitalization and the development of smart agriculture, image classification technology based on deep learning has emerged as a crucial tool for digital monitoring and intelligent prevention a... In the context of rural revitalization and the development of smart agriculture, image classification technology based on deep learning has emerged as a crucial tool for digital monitoring and intelligent prevention and control of agricultural diseases. This paper provides a systematic review of the evolutionary development of algorithms within this field. Addressing challenges such as domain drift and limited global awareness in classical convolutional neural networks (CNNs) applied to complex agricultural environments, the paper focuses on the latest advancements in vision transformers (ViT) and their hybrid architectures to enhance cross-domain robustness and fine-grained recognition capabilities. In response to the challenges posed by scarce long-tail data and limited edge computing power in real-world scenarios, the paper explores solutions related to few-shot learning and ultra-lightweight network deployment. Finally, a forward-looking analysis is presented on the application paradigms of multimodal feature fusion, vision-based large models, and explainable artificial intelligence (AI) within smart plant protection. This analysis aims to offer theoretical insights for the development of efficient and transparent intelligent diagnostic systems for agricultural diseases, thereby supporting the advancement of digital agriculture and the construction of a robust agricultural nation. 展开更多
关键词 Agricultural disease image Classification algorithm Deep learning Research Review
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Optimizing Cancer Classification and Gene Discovery with an Adaptive Learning Search Algorithm for Microarray Analysis 认领 引用
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作者 Chiwen Qu Heng Yao +1 位作者 Tingjiang Pan Zenghui Lu 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第2期901-930,共30页
DNA microarrays, a cornerstone in biomedicine, measure gene expression across thousands to tens of thousands of genes. Identifying the genes vital for accurate cancer classification is a key challenge. Here, we presen... DNA microarrays, a cornerstone in biomedicine, measure gene expression across thousands to tens of thousands of genes. Identifying the genes vital for accurate cancer classification is a key challenge. Here, we present Fs-LSA (F-score based Learning Search Algorithm), a novel gene selection algorithm designed to enhance the precision and efficiency of target gene identification from microarray data for cancer classification. This algorithm is divided into two phases: the first leverages F-score values to prioritize and select feature genes with the most significant differential expression;the second phase introduces our Learning Search Algorithm (LSA), which harnesses swarm intelligence to identify the optimal subset among the remaining genes. Inspired by human social learning, LSA integrates historical data and collective intelligence for a thorough search, with a dynamic control mechanism that balances exploration and refinement, thereby enhancing the gene selection process. We conducted a rigorous validation of Fs-LSA’s performance using eight publicly available cancer microarray expression datasets. Fs-LSA achieved accuracy, precision, sensitivity, and F1-score values of 0.9932, 0.9923, 0.9962, and 0.994, respectively. Comparative analyses with state-of-the-art algorithms revealed Fs-LSA’s superior performance in terms of simplicity and efficiency. Additionally, we validated the algorithm’s efficacy independently using glioblastoma data from GEO and TCGA databases. It was significantly superior to those of the comparison algorithms. Importantly, the driver genes identified by Fs-LSA were instrumental in developing a predictive model as an independent prognostic indicator for glioblastoma, underscoring Fs-LSA’s transformative potential in genomics and personalized medicine. 展开更多
关键词 Gene selection Learning search algorithm Gene expression data Classification
Enhancing Epileptic Seizure Classification via Multi-Feature Fusion in a Transformer-LSTM Architecture 认领 引用
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作者 Gaoteng Yuan Ping Qiu +3 位作者 Qika Lin Jianchu Lin Xiang Li Dongping Gao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1134-1154,共21页
Epilepsy is a chronic neurological disorder characterized by recurrent seizures,posing significant challenges to patients’quality of life.Accurate classification of seizure states is crucial for effective interventio... Epilepsy is a chronic neurological disorder characterized by recurrent seizures,posing significant challenges to patients’quality of life.Accurate classification of seizure states is crucial for effective intervention.This paper presents a deep learning-based approach for epileptic seizure classification by integrating multi-feature analysis of electroencephalogram(EEG)signals.The proposed method begins with signal preprocessing,including denoising,segmentation,and label construction.Subsequently,a comprehensive set of temporal,spectral,and wavelet-based features—such as signal mean,power,heart rate,and wavelet coefficients—is extracted.Feature selection is then performed using the Maximal Information Coefficient(MIC)to identify the most discriminative inputs.A hybrid model combining a Transformer encoder and a Long Short-Term Memory(LSTM)network is developed to effectively capture both long-range dependencies and temporal dynamics in EEG sequences for seizure classification.Evaluated on the Bonn dataset using 5-fold cross-validation,the proposed method achieves an accuracy of 96.43%in distinguishing between epileptic patients and healthy subjects,with a sensitivity of 97.53%in detecting seizure states.It also attains a multi-class classification accuracy of 90.14%across different epileptic signal types.Ablation studies confirm that MICbased feature selection improves accuracy by over 2O%compared to using raw features without selection.The results demonstrate that the integration of multi-feature analysis with the Transformer-LSTM architecture offers an effective and reliable solution for EEG-based seizure classification. 展开更多
关键词 Epileptic classification EEG signal wavelet analysis feature selection transformer-LSTM Algorithm 2:MIC algorithm feature selection
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基于聚类改进MUSIC的多目标方向感知算法 认领 引用
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作者 韩曦 万继银 +2 位作者 李沛阳 毕福昆 司黎明 《华中科技大学学报（自然科学版）》 EI CAS CSCD 北大核心 2026年第3期85-91,共7页
针对智能反射面辅助的感知系统中,基于能量占比的子空间分类准则在低信噪比下极易出现方向角(DOA)多估的问题,提出了一种基于密度聚类改进多信号分类(DENCLUE-MUSIC)的多目标方向感知算法.首先对接收信号自相关矩阵进行特征值分解;然后... 针对智能反射面辅助的感知系统中,基于能量占比的子空间分类准则在低信噪比下极易出现方向角(DOA)多估的问题,提出了一种基于密度聚类改进多信号分类(DENCLUE-MUSIC)的多目标方向感知算法.首先对接收信号自相关矩阵进行特征值分解;然后使用DENCLUE算法估计目标个数,得到噪声子空间;最后使用谱峰搜索获取多个目标的方向角.仿真结果表明:与现有的基于特征值聚类的MUSIC算法相比,本文算法的复杂度更低,计算时间更短,体现了所提DENCLUE-MUSIC算法的优势. 展开更多
关键词 目标感知 角度估计 MUSIC算法 聚类 谱峰搜索
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Feature Selection Optimisation for Cancer Classification Based on Evolutionary Algorithms:An Extensive Review 认领 引用
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作者 Siti Ramadhani Lestari Handayani +4 位作者 Theam Foo Ng Sumayyah Dzulkifly Roziana Ariffin Haldi Budiman Shir Li Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第6期2711-2765,共55页
In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classificati... In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency. 展开更多
关键词 Feature selection(FS) gene expression profile(GEP) cancer classification evolutionary algorithms(EAs) dynamic-length chromosome
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A Novel Hybrid Sine Cosine-Flower Pollination Algorithm for Optimized Feature Selection 认领 引用
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作者 Sumbul Azeem Shazia Javed +3 位作者 Farheen Ibraheem Uzma Bashir Nazar Waheed Khursheed Aurangzeb 《Computers, Materials & Continua》 SCIE EI 2026年第5期1916-1930,共15页
Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset t... Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset to another.Only the relevant features contributemeaningfully to classificationaccuracy.The presence of irrelevant features reduces the system’s effectiveness.Classification performance often deteriorates on high-dimensional datasets due to the large search space.Thus,one of the significant obstacles affecting the performance of the learning process in the majority of machine learning and data mining techniques is the dimensionality of the datasets.Feature selection(FS)is an effective preprocessing step in classification tasks.The aim of applying FS is to exclude redundant and unrelated features while retaining the most informative ones to optimize classification capability and compress computational complexity.In this paper,a novel hybrid binary metaheuristic algorithm,termed hSC-FPA,is proposed by hybridizing the Flower Pollination Algorithm(FPA)and the Sine Cosine Algorithm(SCA).Hybridization controls the exploration capacity of SCA and the exploitation behavior of FPA to maintain a balanced search process.SCA guides the global search in the early iterations,while FPA’s local pollination refines promising solutions in later stages.A binary conversion mechanism using a threshold function is implemented to handle the discrete nature of the feature selection problem.The functionality of the proposed hSC-FPA is authenticated on fourteen standard datasets from the UCI repository using the K-Nearest Neighbors(K-NN)classifier.Experimental results are benchmarked against the standalone SCA and FPA algorithms.The hSC-FPA consistently achieves higher classification accuracy,selects a more compact feature subset,and demonstrates superior convergence behavior.These findings support the stability and outperformance of the hybrid feature selection method presented. 展开更多
关键词 Classification algorithms feature selection process flower pollination algorithm hybrid model metaheuristics multi-objective optimization search algorithm sine cosine algorithm
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Machine learning supervised algorithms for gas hydrate identification and saturation estimation in marine reservoirs using well log data:A case study of NGHP-01-19B 认领 引用
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作者 Yi-fan Wu Zheng Su +5 位作者 Takeshi Tsuji Dai-dai Wu Guang-rong Jin Chao Yang Chuang-ji Feng Neng-you Wu 《China Geology》 CAS CSCD 2026年第3期519-534,I0023-I0027,共16页
Gas hydrates are increasingly recognized as a significant unconventional energy resource and a key factor in marine geohazards and the global carbon cycle.However,accurately identifying and quantifying hydrate-bearing... Gas hydrates are increasingly recognized as a significant unconventional energy resource and a key factor in marine geohazards and the global carbon cycle.However,accurately identifying and quantifying hydrate-bearing formations remains challenging due to complex geophysical signatures and heterogeneous distribution.This study evaluates twelve supervised machine learning(ML)algorithms for two key tasks:Classification of hydrate-bearing layers and regression-based estimation of hydrate saturation,using well log and pore-water geochemical data from Site NGHP-01-19B.Two physically independent labeling frameworks are employed:One based on Archie's law using resistivity(1350 samples,29%hydratebearing),and another based on a three-phase velocity model(890 samples,25%hydrate-bearing).A diverse set of models,including tree-based ensembles(Decision Tree,Random Forest,GBDT,XGBoost,Light GBM,Cat Boost,Bagging,Ada Boost),kernel methods(SVM,SVR),instance-based learning(KNN),neural networks(MLP),and Gaussian Process models(GPR,GPC),are systematically compared using cross-validation and grid search.Ensemble methods consistently performed best in classification,with Ada Boost and GBDT,achieving test accuracies above 0.94(Archie)and 0.98(velocity-based).For regression,GPR delivered the most accurate hydrate saturation estimates(R2>0.99),while GBDT and Random Forest provided a strong balance of accuracy and computational efficiency.Notably,depth below seafloor(TDEP),though not a direct geophysical input,significantly enhanced model performance by acting as a proxy for stratigraphic and thermodynamic conditions.Group-based validation confirmed that random-sample splitting overestimates performance due to depth-wise autocorrelation,highlighting the importance of geologically informed model assessment.Overall,the consistent performance of ML models across both labeling schemes and input feature sets underscores their robustness and transferability,supporting their use as a reliable toolset for offshore gas hydrate reservoir characterization. 展开更多
关键词 Gas hydrate Machine learning algorithm Classification Regression Well log data Archie’s law Marine geohazards Global carbon cycle
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Wheat Leaf Disease Detection with ANOVA-Driven Feature Selection and Whale Optimization Algorithm 认领 引用
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作者 M Chilakarao Santi Kumari Behera +1 位作者 Ashoka Kumar Ratha Prabira Kumar Sethy 《Journal of Harbin Institute of Technology(New Series)》 CAS 2026年第4期73-84,共12页
Early and precise identification of wheat leaf diseases is crucial for sustainable crop management and yield improvement.In this study,we propose a novel hybrid framework that combines deep feature extraction(using Re... Early and precise identification of wheat leaf diseases is crucial for sustainable crop management and yield improvement.In this study,we propose a novel hybrid framework that combines deep feature extraction(using ResNet50 and VGG16)with ANOVA-driven feature selection and whale optimization algorithm(WOA)for hyperparameter tuning of support vector machine(SVM)classifiers.The dataset consists of 40285 wheat leaf images across eight classes(seven disease types and healthy leaves),including augmented samples to address class imbalance.The analysis of variance(ANOVA)method significantly reduced dimensionality by selecting the top 500 most relevant features,while the WOA fine-tuned the SVM to enhance classification performance.The proposed model achieved an impressive accuracy of 98.1%,along with a precision of 97.9%,a recall of 98.0%,and an F1-score of 98.0%on the independent test set.A comparative analysis shows that our method outperforms several state-of-the-art(SOTA)models,including standard CNN and ensemble approaches.This study demonstrates that combining statistical feature selection and bio-inspired optimization with deep learning can substantially advance automated wheat leaf disease detection,offering promising applications for precision agriculture. 展开更多
关键词 wheat leaf disease classification deep learning ResNet50 VGG16 support vector machine ANOVA feature selection whale optimization algorithm
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基于MUSIC算法的无线通信测向系统设计与仿真 认领 引用
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作者 刘一鸣 刘开敏 何涛 《移动信息》 2026年第4期1-3,25,共3页
MUSIC算法是一种适用于阵列信号处理的高分辨波达方向估计算法。文中探讨了无线通信中的空间信号参数估计方法,并基于MATLAB对多信号分类MUSIC测向算法展开了设计与仿真。实验结果表明,基于MUSIC算法的测向系统可以实现对多信号源DOA的... MUSIC算法是一种适用于阵列信号处理的高分辨波达方向估计算法。文中探讨了无线通信中的空间信号参数估计方法,并基于MATLAB对多信号分类MUSIC测向算法展开了设计与仿真。实验结果表明,基于MUSIC算法的测向系统可以实现对多信号源DOA的高精度、高分辨率估计,且具有较好性能。 展开更多
关键词 MUSIC算法 DOA估计 平滑差分
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基于圆阵干涉仪改进秩亏损极化MUSIC测向算法 认领 引用
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作者 何勇 陈华庆 《舰船电子对抗》 2026年第1期72-79,共8页
圆阵干涉仪测向系统是基于电磁信号波达方向(DOA)估计,实现不同极化入射信号的方位角测量。针对传统极化及秩亏损极化多重信号分类(MUSIC)算法对天线误差、阵元互耦等非理想因素敏感导致测向精度下降的问题,提出一种改进秩亏损极化MUSI... 圆阵干涉仪测向系统是基于电磁信号波达方向(DOA)估计,实现不同极化入射信号的方位角测量。针对传统极化及秩亏损极化多重信号分类(MUSIC)算法对天线误差、阵元互耦等非理想因素敏感导致测向精度下降的问题,提出一种改进秩亏损极化MUSIC算法。该算法利用数据重构极化-空域联合导向矢量,实现实时DOA估计。实验结果表明,所提算法能有效克服多种实际误差影响,显著提高了不同条件下的测向精度。 展开更多
关键词 圆阵干涉仪 改进秩亏损 极化-空域联合导向矢量 MUSIC算法
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Prediction of geological characteristics from shield operational parameters by integrating grid search and K-fold cross validation into stacking classification algorithm 认领 引用 被引量：26
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作者 Tao Yan Shui-Long Shen +1 位作者 Annan Zhou Xiangsheng Chen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2022年第4期1292-1303,共12页
This study presents a framework for predicting geological characteristics based on integrating a stacking classification algorithm(SCA) with a grid search(GS) and K-fold cross validation(K-CV). The SCA includes two le... This study presents a framework for predicting geological characteristics based on integrating a stacking classification algorithm(SCA) with a grid search(GS) and K-fold cross validation(K-CV). The SCA includes two learner layers: a primary learner’s layer and meta-classifier layer. The accuracy of the SCA can be improved by using the GS and K-CV. The GS was developed to match the hyper-parameters and optimise complicated problems. The K-CV is commonly applied to changing the validation set in a training set. In general, a GS is usually combined with K-CV to produce a corresponding evaluation index and select the best hyper-parameters. The torque penetration index(TPI) and field penetration index(FPI) are proposed based on shield parameters to express the geological characteristics. The elbow method(EM) and silhouette coefficient(Si) are employed to determine the types of geological characteristics(K) in a Kmeans++ algorithm. A case study on mixed ground in Guangzhou is adopted to validate the applicability of the developed model. The results show that with the developed framework, the four selected parameters, i.e. thrust, advance rate, cutterhead rotation speed and cutterhead torque, can be used to effectively predict the corresponding geological characteristics. 展开更多
关键词 Geological characteristics Stacking classification algorithm(SCA) K-fold cross-validation(K-CV) K-means++
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Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm 认领 引用 被引量：5
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作者 Shao-Jun Zhu Hao-Dong Zhan +4 位作者 Mao-Nian Wu Bo Zheng Bang-Quan Liu Shao-Chong Zhang Wei-Hua Yang 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2023年第7期995-1004,共10页
AIM:To conduct a classification study of high myopic maculopathy(HMM)using limited datasets,including tessellated fundus,diffuse chorioretinal atrophy,patchy chorioretinal atrophy,and macular atrophy,and minimize anno... AIM:To conduct a classification study of high myopic maculopathy(HMM)using limited datasets,including tessellated fundus,diffuse chorioretinal atrophy,patchy chorioretinal atrophy,and macular atrophy,and minimize annotation costs,and to optimize the ALFA-Mix active learning algorithm and apply it to HMM classification.METHODS:The optimized ALFA-Mix algorithm(ALFAMix+)was compared with five algorithms,including ALFA-Mix.Four models,including Res Net18,were established.Each algorithm was combined with four models for experiments on the HMM dataset.Each experiment consisted of 20 active learning rounds,with 100 images selected per round.The algorithm was evaluated by comparing the number of rounds in which ALFA-Mix+outperformed other algorithms.Finally,this study employed six models,including Efficient Former,to classify HMM.The best-performing model among these models was selected as the baseline model and combined with the ALFA-Mix+algorithm to achieve satisfactor y classification results with a small dataset.RESULTS:ALFA-Mix+outperforms other algorithms with an average superiority of 16.6,14.75,16.8,and 16.7 rounds in terms of accuracy,sensitivity,specificity,and Kappa value,respectively.This study conducted experiments on classifying HMM using several advanced deep learning models with a complete training set of 4252 images.The Efficient Former achieved the best results with an accuracy,sensitivity,specificity,and Kappa value of 0.8821,0.8334,0.9693,and 0.8339,respectively.Therefore,by combining ALFA-Mix+with Efficient Former,this study achieved results with an accuracy,sensitivity,specificity,and Kappa value of 0.8964,0.8643,0.9721,and 0.8537,respectively.CONCLUSION:The ALFA-Mix+algorithm reduces the required samples without compromising accuracy.Compared to other algorithms,ALFA-Mix+outperforms in more rounds of experiments.It effectively selects valuable samples compared to other algorithms.In HMM classification,combining ALFA-Mix+with Efficient Former enhances model performance,further demonstrating the effectiveness of ALFA-Mix+. 展开更多
关键词 high myopic maculopathy deep learning active learning image classification ALFA-Mix algorithm
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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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FLIGHT CLASSIFICATION MODEL BASED ON TRANSITIVE CLOSURE ALGORITHM AND APPLICATION TO FLIGHT SEQUENCING PROBLEM 认领 引用 被引量：4
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作者 李雄 徐肖豪 李冬宾 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2007年第1期31-35,共5页
A new arrival and departure flight classification method based on the transitive closure algorithm （TCA） is proposed. Firstly, the fuzzy set theory and the transitive closure algorithm are introduced. Then four diff... A new arrival and departure flight classification method based on the transitive closure algorithm （TCA） is proposed. Firstly, the fuzzy set theory and the transitive closure algorithm are introduced. Then four different factors are selected to establish the flight classification model and a method is given to calculate the delay cost for each class. Finally, the proposed method is implemented in the sequencing problems of flights in a terminal area, and results are compared with that of the traditional classification method（TCM）. Results show that the new classification model is effective in reducing the expenses of flight delays, thus optimizing the sequences of arrival and departure flights, and improving the efficiency of air traffic control. 展开更多
关键词 air traffic control transitive closure algorithm cost of flight delay classification model
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Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification 认领 引用 被引量：2
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作者 FU Xiaoyang P E R Dale ZHANG Shuqing 《Chinese Geographical Science》 SCIE 2008年第2期162-170,共9页
Coastal wetlands are characterized by complex patterns both in their geomorphlc and ecological teatures. Besides field observations, it is necessary to analyze the land cover of wetlands through the color infrared （... Coastal wetlands are characterized by complex patterns both in their geomorphlc and ecological teatures. Besides field observations, it is necessary to analyze the land cover of wetlands through the color infrared （CIR） aerial photography or remote sensing image. In this paper, we designed an evolving neural network classifier using variable string genetic algorithm （VGA） for the land cover classification of CIR aerial image. With the VGA, the classifier that we designed is able to evolve automatically the appropriate number of hidden nodes for modeling the neural network topology optimally and to find a near-optimal set of connection weights globally. Then, with backpropagation algorithm （BP）, it can find the best connection weights. The VGA-BP classifier, which is derived from hybrid algorithms mentioned above, is demonstrated on CIR images classification effectively. Compared with standard classifiers, such as Bayes maximum-likelihood classifier, VGA classifier and BP-MLP （multi-layer perception） classifier, it has shown that the VGA-BP classifier can have better performance on highly resolution land cover classification. 展开更多
关键词 variable string genetic algorithm neural network pattern classification CIR image
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An Experimental Comparative Study on Three Classification Algorithms 认领 引用 被引量：1
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作者 蔡巍 王永成 +1 位作者 李伟 尹中航 《Journal of Shanghai Jiaotong university(Science)》 2003年第2期133-136,共4页
Classification algorithm is one of the key techniques to affect text automatic classification system’s performance, play an important role in automatic classification research area. This paper comparatively analyzed ... Classification algorithm is one of the key techniques to affect text automatic classification system’s performance, play an important role in automatic classification research area. This paper comparatively analyzed k-NN. VSM and hybrid classification algorithm presented by our research group. Some 2000 pieces of Internet news provided by ChinaInfoBank are used in the experiment. The result shows that the hybrid algorithm’s performance presented by the groups is superior to the other two algorithms. 展开更多
关键词 classification algorithm k-NN VSM hybrid algorithm
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A RBF classification method of remote sensing image based on genetic algorithm 认领 引用 被引量：1
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作者 万鲁河 张思冲 +1 位作者 刘万宇 臧淑英 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2006年第6期711-714,共4页
The remote sensing image classification has stimulated considerable interest as an effective method for better retrieving information from the rapidly increasing large volume, complex and distributed satellite remote ... The remote sensing image classification has stimulated considerable interest as an effective method for better retrieving information from the rapidly increasing large volume, complex and distributed satellite remote imaging data of large scale and cross-time, due to the increase of remote image quantities and image resolutions. In the paper, the genetic algorithms were employed to solve the weighting of the radial basis faction networks in order to improve the precision of remote sensing image classification. The remote sensing image classification was also introduced for the GIS spatial analysis and the spatial online analytical processing (OLAP), and the resulted effectiveness was demonstrated in the analysis of land utilization variation of Daqing city. 展开更多
关键词 genetic algorithm radial basis function networks remote sensing image classification spatial online analytical processing GIS
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Enhancing subsurface seismic profiling with distributed acoustic sensing and optimization algorithms 认领 引用 被引量：2
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作者 Jing Wang Hong-Hu Zhu +4 位作者 Gang Cheng Tao Wang Xu-Long Gong Dao-Yuan Tan Bin Shi 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第6期3632-3643,共12页
The distribution of shear-wave velocities in the subsurface is generally used to assess the potential forseismic liquefaction and soil amplification effects and to classify seismic sites. Newly developeddistributed ac... The distribution of shear-wave velocities in the subsurface is generally used to assess the potential forseismic liquefaction and soil amplification effects and to classify seismic sites. Newly developeddistributed acoustic sensing (DAS) technology enables estimation of the shear-wave distribution as ahigh-density seismic observation system. This technology is characterized by low maintenance costs,high-resolution outputs, and real-time data transmission capabilities, albeit with the challenge ofmanaging massive data generation. Rapid and efficient interpretation of data is the key to advancingapplication of the DAS technology. In this study, field tests were carried out to record ambient noise overa short period using DAS technology, from which the surface-wave dispersion curves were extracted. Inorder to reduce the influence of directional effects on the results, an unsupervised clustering method isused to select appropriate clusters to extract the Green's function. A combination of a genetic algorithmand Monte Carlo (GA-MC) simulation is proposed to invert the subsurface velocity structure. Thestratigraphic profiles obtained by the GA-MC method are in agreement with the borehole profiles.Compared to other methods, the proposed optimization method not only improves the solution qualitybut also reduces the solution time. 展开更多
关键词 Shallow subsurface velocity Site classification Ambient noise imaging Distributed acoustic sensing(DAS) Genetic algorithms and Monte Carlo simulation
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MUSIC ALGORITHM FOR LOCATING POINT-LIKE SCATTERERS CONTAINED IN A SAMPLE ON FLAT SUBSTRATE 认领 引用
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作者 董和平 马富明 张德悦 《Acta Mathematica Scientia》 SCIE CSCD 2012年第4期1647-1661,共15页
In this paper, we consider a MUSIC algorithm for locating point-like scatterers contained in a sample on flat substrate. Based on an asymptotic expansion of the scattering amplitude proposed by Ammari et al., the reco... In this paper, we consider a MUSIC algorithm for locating point-like scatterers contained in a sample on flat substrate. Based on an asymptotic expansion of the scattering amplitude proposed by Ammari et al., the reconstruction problem can be reduced to a calculation of Green function corresponding to the background medium. In addition, we use an explicit formulation of Green function in the MUSIC algorithm to simplify the calculation when the cross-section of sample is a half-disc. Numerical experiments are included to demonstrate the feasibility of this method. 展开更多
关键词 MUSIC algorithm Hemholtz equation multi static response （MSR） matrix Green function
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