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Recursive Bayesian Algorithm for Identification of Systems with Non-uniformly Sampled Input Data 认领 引用 被引量：3
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作者 Shao-Xue Jing Tian-Hong Pan Zheng-Ming Li 《International Journal of Automation and computing》 CSCD 2018年第3期335-344,共10页
To identify systems with non-uniformly sampled input data, a recursive Bayesian identification algorithm with covariance resetting is proposed. Using estimated noise transfer function as a dynamic filter, the system w... To identify systems with non-uniformly sampled input data, a recursive Bayesian identification algorithm with covariance resetting is proposed. Using estimated noise transfer function as a dynamic filter, the system with colored noise is transformed into the system with white noise. In order to improve estimates, the estimated noise variance is employed as a weighting factor in the algorithm. Meanwhile, a modified covariance resetting method is also integrated in the proposed algorithm to increase the convergence rate. A numerical example and an industrial example validate the proposed algorithm. 展开更多
关键词 Parameter estimation discrete time systems Gaussian noise Bayesian algorithm covariance resetting.
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Web multimedia information retrieval using improved Bayesian algorithm 认领 引用 被引量：3
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作者 余铁军 陈纯 +1 位作者 余铁民 林怀忠 《Journal of Zhejiang University Science》 2003年第4期415-420,共6页
The main thrust of this paper is application of a novel data mining approach on the log of user' s feedback to improve web multimedia information retrieval performance. A user space model was constructed based on ... The main thrust of this paper is application of a novel data mining approach on the log of user' s feedback to improve web multimedia information retrieval performance. A user space model was constructed based on data mining, and then integrated into the original information space model to improve the accuracy of the new information space model. It can remove clutter and irrelevant text information and help to eliminate mismatch between the page author' s expression and the user' s understanding and expectation. User spacemodel was also utilized to discover the relationship between high-level and low-level features for assigning weight. The authors proposed improved Bayesian algorithm for data mining. Experiment proved that the au-thors' proposed algorithm was efficient. 展开更多
关键词 Relevant feedback Web log mining Improved Bayesian algorithm User space model
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Bayesian-based ant colony optimization algorithm for edge detection 认领 引用
3
作者 YU Yongbin ZHONG Yuanjingyang +6 位作者 FENG Xiao WANG Xiangxiang FAVOUR Ekong ZHOU Chen CHENG Man WANG Hao WANG Jingya 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第4期892-902,共11页
Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of t... Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task. 展开更多
关键词 ant colony optimization(ACO) Bayesian algorithm edge detection transfer function.
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Geophysics-informed stratigraphic modeling using spatial sequential Bayesian updating algorithm 认领 引用
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作者 Wei Yan Shouyong Yi +3 位作者 Taosheng Huang Jie Zou Wan-Huan Zhou Ping Shen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第7期4400-4412,共13页
Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-eff... Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-effective geophysical technique can acquire high-density data;however,uncertainty and nonuniqueness inherent in ERT impede its usage for stratigraphy identification.This paper integrates ERT and onsite observations for the first time to propose a novel method for characterizing stratigraphic profiles.The method consists of two steps:(1)ERT for prior knowledge:ERT data are processed by soft clustering using the Gaussian mixture model,followed by probability smoothing to quantify its depthdependent uncertainty;and(2)Observations for calibration:a spatial sequential Bayesian updating(SSBU)algorithm is developed to update the prior knowledge based on likelihoods derived from onsite observations,namely topsoil and boreholes.The effectiveness of the proposed method is validated through its application to a real slope site in Foshan,China.Comparative analysis with advanced borehole-driven methods highlights the superiority of incorporating ERT data in stratigraphic modeling,in terms of prediction accuracy at borehole locations and sensitivity to borehole data.Informed by ERT,reduced sensitivity to boreholes provides a fundamental solution to the longstanding challenge of sparse measurements.The paper further discusses the impact of ERT uncertainty on the proposed model using time-lapse measurements,the impact of model resolution,and applicability in engineering projects.This study,as a breakthrough in stratigraphic modeling,bridges gaps in combining geophysical and geotechnical data to address measurement sparsity and paves the way for more economical geotechnical exploration. 展开更多
关键词 Stratigraphic modeling Electrical resistivity tomography(ERT) Site characterization Spatial sequential Bayesian updating(SSBU)algorithm Sparse measurements
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Bayesian-based analysis of sequence activity characteristics in the Bohai Rim region 认领 引用
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作者 Bi Jin-Meng Song Cheng Cao Fu-Yang 《Applied Geophysics》 SCIE CSCD 2025年第2期237-251,554,共15页
Disaster mitigation necessitates scientifi c and accurate aftershock forecasting during the critical 2 h after an earthquake. However, this action faces immense challenges due to the lack of early postearthquake data ... Disaster mitigation necessitates scientifi c and accurate aftershock forecasting during the critical 2 h after an earthquake. However, this action faces immense challenges due to the lack of early postearthquake data and the unreliability of forecasts. To obtain foundational data for sequence parameters of the land-sea adjacent zone and establish a reliable and operational aftershock forecasting framework, we combined the initial sequence parameters extracted from envelope functions and incorporated small-earthquake information into our model to construct a Bayesian algorithm for the early postearthquake stage. We performed parameter fitting and early postearthquake aftershock occurrence rate forecasting and effectiveness evaluation for 36 earthquake sequences with M ≥ 4.0 in the Bohai Rim region since 2010. According to the results, during the early stage after the mainshock, earthquake sequence parameters exhibited relatively drastic fl uctuations with signifi cant errors. The integration of prior information can mitigate the intensity of these changes and reduce errors. The initial and stable sequence parameters generally display advantageous distribution characteristics, with each parameter’s distribution being relatively concentrated and showing good symmetry and remarkable consistency. The sequence parameter p-values were relatively small, which indicates the comparatively slow attenuation of signifi cant earthquake events in the Bohai Rim region. A certain positive correlation was observed between earthquake sequence parameters b and p. However, sequence parameters are unrelated to the mainshock magnitude, which implies that their statistical characteristics and trends are universal. The Bayesian algorithm revealed a good forecasting capability for aftershocks in the early postearthquake period (2 h) in the Bohai Rim region, with an overall forecasting effi cacy rate of 76.39%. The proportion of “too low” failures exceeded that of “too high” failures, and the number of forecasting failures for the next three days was greater than that for the next day. 展开更多
关键词 earthquake sequences Bayesian algorithm model parameters correlation analysis effectiveness evaluation
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Well production optimization using streamline features-based objective function and Bayesian adaptive direct search algorithm 认领 引用 被引量：10
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作者 Qi-Hong Feng Shan-Shan Li +2 位作者 Xian-Min Zhang Xiao-Fei Gao Ji-Hui Ni 《Petroleum Science》 SCIE CAS CSCD 2022年第6期2879-2894,共16页
Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.T... Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development. 展开更多
关键词 Well production Optimization efficiency Streamline simulation Streamline feature Objective function Bayesian adaptive direct search algorithm
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Self-Organizing Genetic Algorithm Based Method for Constructing Bayesian Networks from Databases 认领 引用
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作者 郑建军 刘玉树 陈立潮 《Journal of Beijing Institute of Technology》 EI CAS 2003年第1期23-27,共5页
The typical characteristic of the topology of Bayesian networks (BNs) is the interdependence among different nodes (variables), which makes it impossible to optimize one variable independently of others, and the learn... The typical characteristic of the topology of Bayesian networks (BNs) is the interdependence among different nodes (variables), which makes it impossible to optimize one variable independently of others, and the learning of BNs structures by general genetic algorithms is liable to converge to local extremum. To resolve efficiently this problem, a self-organizing genetic algorithm (SGA) based method for constructing BNs from databases is presented. This method makes use of a self-organizing mechanism to develop a genetic algorithm that extended the crossover operator from one to two, providing mutual competition between them, even adjusting the numbers of parents in recombination (crossoverecomposition) schemes. With the K2 algorithm, this method also optimizes the genetic operators, and utilizes adequately the domain knowledge. As a result, with this method it is able to find a global optimum of the topology of BNs, avoiding premature convergence to local extremum. The experimental results proved to be and the convergence of the SGA was discussed. 展开更多
关键词 Bayesian networks structure learning from databases self-organizing genetic algorithm
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Air Combat Assignment Problem Based on Bayesian Optimization Algorithm 认领 引用 被引量：3
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作者 FU LI LONG XI HE WENBIN 《Journal of Shanghai Jiaotong university(Science)》 EI 2022年第6期799-805,共7页
In order to adapt to the changing battlefield situation and improve the combat effectiveness of air combat,the problem of air battle allocation based on Bayesian optimization algorithm(BOA)is studied.First,we discuss ... In order to adapt to the changing battlefield situation and improve the combat effectiveness of air combat,the problem of air battle allocation based on Bayesian optimization algorithm(BOA)is studied.First,we discuss the number of fighters on both sides,and apply cluster analysis to divide our fighter into the same number of groups as the enemy.On this basis,we sort each of our fighters'different advantages to the enemy fighters,and obtain a series of target allocation schemes for enemy attacks by first in first serviced criteria.Finally,the maximum advantage function is used as the target,and the BOA is used to optimize the model.The simulation results show that the established model has certain decision-making ability,and the BOA can converge to the global optimal solution at a faster speed,which can effectively solve the air combat task assignment problem. 展开更多
关键词 air combat task assignment first in first serviced criteria Bayesian optimization algorithm(BOA)
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Target distribution in cooperative combat based on Bayesian optimization algorithm 认领 引用 被引量：6
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作者 Shi Zhi fu Zhang An Wang Anli 《Journal of Systems Engineering and Electronics》 2006年第2期339-342,共4页
Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can ... Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can estimate the joint probability distribution of the variables with Bayesian network, and the new candidate solutions also can be generated by the joint distribution. The simulation example verified that the method could be used to solve the complex question, the operation was quickly and the solution was best. 展开更多
关键词 target distribution Bayesian network Bayesian optimization algorithm cooperative air combat.
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面向运动想象脑电分类的BayesianGCN算法研究 认领 引用 被引量：1
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作者 李亚茹 张悦 +2 位作者 马琛 赵路清 郭一娜 《太原科技大学学报》 2025年第2期113-119,共7页
为了提升运动想象脑机接口任务分类的准确性,充分利用脑电信号的时空特性,构建了贝叶斯图卷积网络。将贝叶斯算法嵌入图卷积神经网络,对网络中的权重进行概率建模,使得贝叶斯图卷积网络能够根据输入信号动态调整权重,以更好地适应不同... 为了提升运动想象脑机接口任务分类的准确性,充分利用脑电信号的时空特性,构建了贝叶斯图卷积网络。将贝叶斯算法嵌入图卷积神经网络,对网络中的权重进行概率建模,使得贝叶斯图卷积网络能够根据输入信号动态调整权重,以更好地适应不同的信号特性,提高模型的分类准确性,泛化性和可解释性。该模型在两个公开脑机接口竞赛数据集上取得的平均分类准确率分别可达97.20%和95.17%,Kappa系数分别可达0.967 9和0.940 0.实验结果表明该方法能有效提高运动想象任务分类精度,且具有较好的泛化性和可解释性。 展开更多
关键词 脑机接口 运动想象 贝叶斯算法 图卷积网络
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基于Bayesian-Bagging-XGBoost算法的GFRP增强混凝土柱轴向承载力预测 认领 引用
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作者 唐培根 李小亮 +2 位作者 何鑫 马国辉 张祥 《复合材料科学与工程》 CAS 北大核心 2025年第9期98-109,共12页
由于钢筋与玻璃纤维增强聚合物(Glass Fiber Reinforced Polymer,GFRP)筋力学特性的差异,GFRP筋增强混凝土柱轴压承载力计算不能简单套用钢筋混凝土柱计算方法。为提高GFRP筋增强混凝土柱轴压承载力预测模型的准确性,以253组试验数据作... 由于钢筋与玻璃纤维增强聚合物(Glass Fiber Reinforced Polymer,GFRP)筋力学特性的差异,GFRP筋增强混凝土柱轴压承载力计算不能简单套用钢筋混凝土柱计算方法。为提高GFRP筋增强混凝土柱轴压承载力预测模型的准确性,以253组试验数据作为极限梯度提升(XGBoost)算法建模的数据基础,并采用Bayesian优化算法、Bagging算法对XGBoost算法进行了优化,以提高模型的预测精度、稳定性和训练效率。采用决定系数(R2)、平均绝对误差(MAE)和相对根均方误差(RRSE)等指标对模型进行评价,并将其与现有预测模型进行对比分析。研究发现,Bayesian优化算法和Bagging算法可有效提高模型的训练效率、预测精度。所提出的Bayesian-Bagging-XGBoost模型的R2,MAE,RRSE值分别为0.6916,418.1629,0.5553,远优于现有预测模型指标,可为GFRP筋增强混凝土柱的工程应用提供更加准确的参考。 展开更多
关键词 Bayesian优化 XGBoost算法 GFRP增强混凝土柱 轴向承载力 预测
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Data-driven production optimization using particle swarm algorithm based on the ensemble-learning proxy model 认领 引用 被引量：5
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作者 Shu-Yi Du Xiang-Guo Zhao +4 位作者 Chi-Yu Xie Jing-Wei Zhu Jiu-Long Wang Jiao-Sheng Yang Hong-Qing Song 《Petroleum Science》 SCIE EI CAS CSCD 2023年第5期2951-2966,共16页
Production optimization is of significance for carbonate reservoirs,directly affecting the sustainability and profitability of reservoir development.Traditional physics-based numerical simulations suffer from insuffic... Production optimization is of significance for carbonate reservoirs,directly affecting the sustainability and profitability of reservoir development.Traditional physics-based numerical simulations suffer from insufficient calculation accuracy and excessive time consumption when performing production optimization.We establish an ensemble proxy-model-assisted optimization framework combining the Bayesian random forest(BRF)with the particle swarm optimization algorithm(PSO).The BRF method is implemented to construct a proxy model of the injectioneproduction system that can accurately predict the dynamic parameters of producers based on injection data and production measures.With the help of proxy model,PSO is applied to search the optimal injection pattern integrating Pareto front analysis.After experimental testing,the proxy model not only boasts higher prediction accuracy compared to deep learning,but it also requires 8 times less time for training.In addition,the injection mode adjusted by the PSO algorithm can effectively reduce the gaseoil ratio and increase the oil production by more than 10% for carbonate reservoirs.The proposed proxy-model-assisted optimization protocol brings new perspectives on the multi-objective optimization problems in the petroleum industry,which can provide more options for the project decision-makers to balance the oil production and the gaseoil ratio considering physical and operational constraints. 展开更多
关键词 Production optimization Random forest The Bayesian algorithm Ensemble learning Particle swarm optimization
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Within-Project and Cross-Project Software Defect Prediction Based on Improved Transfer Naive Bayes Algorithm 认领 引用 被引量：4
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作者 Kun Zhu Nana Zhang +1 位作者 Shi Ying Xu Wang 《Computers, Materials & Continua》 SCIE EI 2020年第5期891-910,共20页
With the continuous expansion of software scale,software update and maintenance have become more and more important.However,frequent software code updates will make the software more likely to introduce new defects.So... With the continuous expansion of software scale,software update and maintenance have become more and more important.However,frequent software code updates will make the software more likely to introduce new defects.So how to predict the defects quickly and accurately on the software change has become an important problem for software developers.Current defect prediction methods often cannot reflect the feature information of the defect comprehensively,and the detection effect is not ideal enough.Therefore,we propose a novel defect prediction model named ITNB(Improved Transfer Naive Bayes)based on improved transfer Naive Bayesian algorithm in this paper,which mainly considers the following two aspects:(1)Considering that the edge data of the test set may affect the similarity calculation and final prediction result,we remove the edge data of the test set when calculating the data similarity between the training set and the test set;(2)Considering that each feature dimension has different effects on defect prediction,we construct the calculation formula of training data weight based on feature dimension weight and data gravity,and then calculate the prior probability and the conditional probability of training data from the weight information,so as to construct the weighted bayesian classifier for software defect prediction.To evaluate the performance of the ITNB model,we use six datasets from large open source projects,namely Bugzilla,Columba,Mozilla,JDT,Platform and PostgreSQL.We compare the ITNB model with the transfer Naive Bayesian(TNB)model.The experimental results show that our ITNB model can achieve better results than the TNB model in terms of accurary,precision and pd for within-project and cross-project defect prediction. 展开更多
关键词 Cross-project defect prediction transfer Naive Bayesian algorithm edge data similarity calculation feature dimension weight
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海上船舶碰撞事故关联规则挖掘及致因分析 认领 引用 被引量：2
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作者 田延飞 何琪 +2 位作者 滑林 张俊 牟军敏 《安全与环境学报》 CAS CSCD 北大核心 2026年第5期1682-1688,共7页
为探究海上船舶碰撞事故致因及事故演化规则,收集浙江沿海海上交通事故案例213例,基于人为因素分析与分类系统框架,从组织影响、不安全监督、不安全行为的前提条件、船员不安全行为4个层面识别得到26个事故致因。利用Apriori算法挖掘得... 为探究海上船舶碰撞事故致因及事故演化规则,收集浙江沿海海上交通事故案例213例,基于人为因素分析与分类系统框架,从组织影响、不安全监督、不安全行为的前提条件、船员不安全行为4个层面识别得到26个事故致因。利用Apriori算法挖掘得到船舶碰撞事故较强关联规则781条;基于支持度、置信度、提升度等每项指标下的前10条强关联规则,得到瞭望疏忽A2、未使用导助航设备A4、碰撞估计不充分A6、未采取有效避碰措施A9是船舶碰撞事故的4项重要致因。构建船舶碰撞事故贝叶斯网络模型;进行最大致因路径与敏感性分析,得到:“未使用导助航设备→瞭望疏忽→船舶碰撞”为最大致因路径,“未使用导助航设备”的敏感值最大。研究可为识别海上船舶碰撞事故致因、认识事故发展过程和有针对性地采取事故风险防范措施等提供支持。 展开更多
关键词 安全社会工程 事故致因 人为因素分析和分类系统 关联规则 Apriori算法 贝叶斯网络
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基于种群多样性和互信息混合引导的贝叶斯网络结构学习算法 认领 引用 被引量：1
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作者 方伟 吴昀霖 朱书伟 《控制与决策》 EI CSCD 北大核心 2026年第4期1077-1088,共12页
贝叶斯网络(BN)是一种概率图模型,用于表示不确定的因果关系.由于解空间的数量随着变量数量增长呈超指数增长,使得贝叶斯网络结构学习(BNSL)成为NP难问题.遗传算法(GA)可以高效地在空间中搜索更多可能的结构组合,在BNSL问题中取得了诸... 贝叶斯网络(BN)是一种概率图模型,用于表示不确定的因果关系.由于解空间的数量随着变量数量增长呈超指数增长,使得贝叶斯网络结构学习(BNSL)成为NP难问题.遗传算法(GA)可以高效地在空间中搜索更多可能的结构组合,在BNSL问题中取得了诸多成果,但是仍然存在过早收敛,结构准确率不高等问题.鉴于此,提出一种基于种群多样性和互信息混合引导的贝叶斯网络结构学习算法(DM-GABN).在去环阶段,使用翻转-删除-修复混合操作代替删除边以保留更多样的基因型;在选择算子阶段,根据当前种群多样性动态调整种群年龄阈值,淘汰衰老个体,维持合理的种群年龄结构;在交叉策略中,引入生物学的基因型频率概念,保护低频结构的同时利用互信息限制搜索空间大小并引导搜索.在10个标准BN数据集上对DM-GABN进行实验评估,并与包含最先进方法在内的10种BNSL方法进行对比.实验结果显示,所提出方法学习的BN结构准确率更高,算法收敛速度更快. 展开更多
关键词 贝叶斯网络 遗传算法 结构学习 种群多样性 互信息 基因型频率
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基于贝叶斯优化神经网络的Cu-SiC镀层镀速预测 认领 引用 被引量：1
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作者 魏波 刘翠芳 吕焦盛 《电镀与精饰》 CAS 北大核心 2026年第1期123-130,共8页
Cu-SiC镀层镀速受多种因素影响,包括电流密度、镀液成分、温度、搅拌速度等,这些因素与镀速之间存在着复杂的非线性关系。传统的神经网络模型只能处理线性关系,对于复杂的电镀数据特征之间的非线性关系以及时空特性难以有效捕捉,影响了... Cu-SiC镀层镀速受多种因素影响,包括电流密度、镀液成分、温度、搅拌速度等,这些因素与镀速之间存在着复杂的非线性关系。传统的神经网络模型只能处理线性关系,对于复杂的电镀数据特征之间的非线性关系以及时空特性难以有效捕捉,影响了模型超参数的优化速度及预测精度。为此,提出基于贝叶斯优化神经网络的Cu-SiC镀层镀速预测方法。该方法系统性地采集电镀过程中的电流值、镀液温度、镀液pH值、SiC粒子浓度、镀液搅拌速率数据,并采用Z-score标准化方法对每种电镀数据进行归一化处理,以促进模型在不同特征间的有效比较。设计贝叶斯优化神经网络的BO-CNN-LSTM模型,将各种电镀数据的归一化处理结果作为模型输入,同时捕捉电镀数据的空间特征和时间依赖性,利用贝叶斯算法优化层自动搜索模型最优超参数组合。利用最优超参数组合实施模型训练,最终实现Cu-SiC镀层镀速的高效精准预测。实验结果表明,经过贝叶斯算法优化超参数后,该预测方法的决定系数R2显著提升,更接近1。预测结果与实际镀速之间的偏差较小,曲线走势与实际镀速高度一致。此外,该方法的CPU使用率也相对较低。 展开更多
关键词 电镀数据 Z-score标准化 贝叶斯优化算法 BO-CNN-LSTM模型 Cu-SiC镀层 镀速预测
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Multi-sources information fusion algorithm in airborne detection systems 认领 引用 被引量：18
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作者 Yang Yan Jing Zhanrong Gao Tan Wang Huilong 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第1期171-176,共6页
To aim at the multimode character of the data from the airplane detecting system, the paper combines Dempster- Shafer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode ... To aim at the multimode character of the data from the airplane detecting system, the paper combines Dempster- Shafer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode data fusion algorithm. The algorithm adopts a prorated algorithm relate to the incertitude evaluation to convert the probability evaluation into the precognition probability in an identity frame, and ensures the adaptability of different data from different source to the mixed system. To guarantee real time fusion, a combination of time domain fusion and space domain fusion is established, this not only assure the fusion of data chain in different time of the same sensor, but also the data fusion from different sensors distributed in different platforms and the data fusion among different modes. The feasibility and practicability are approved through computer simulation. 展开更多
关键词 Information fusion Dempster-Shafer evidence theory Subjective Bayesian algorithm Airplane detecting system
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基于贝叶斯算法的致密储层压驱参数智能优化及其应用 认领 引用
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作者 马莅 卢聪 +3 位作者 郭建春 雍锐 吴建发 曾波 《大庆石油地质与开发》 CAS 北大核心 2026年第3期91-99,共9页
致密储层压驱机理认识不清、工程参数缺乏科学高效调控,导致压驱实施过程中驱替受效方向明显、井组水窜态势严重、生产井产能难以提升。通过引入损伤因子d,基于非线性渗流模型和Biot线弹性理论,构建压驱注水条件下局部化微破裂损伤模型... 致密储层压驱机理认识不清、工程参数缺乏科学高效调控,导致压驱实施过程中驱替受效方向明显、井组水窜态势严重、生产井产能难以提升。通过引入损伤因子d,基于非线性渗流模型和Biot线弹性理论,构建压驱注水条件下局部化微破裂损伤模型,考虑化学渗透压以及驱油剂影响,建立耦合渗流-应力-损伤的致密储层压驱数学模型;融合贝叶斯优化算法,创新压驱工程参数优化方法,加快搜索速度、提高计算精度、提升优化效率;建立不同地质参数条件下压驱工程参数优化图版,推荐单层压驱注入量为(3.0~3.5)×104m3,注入速度为1000~1200 m3/d,闷井时间为20~30 d,驱油剂体积分数为0.15%~0.2%。研究成果在渤海湾盆地BN区块致密油藏压驱开发中成功应用,较邻区单井日产油量提高6.1~7.2 t,含水率降低11.5~40.0百分点,支撑油田压驱现场顺利、高效实施。 展开更多
关键词 致密储层 压驱工艺 工程参数 贝叶斯算法 数值模拟
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基于贝叶斯优化LightGBM算法的深层页岩储层分级评价 认领 引用
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作者 张鲁川 李一博 +6 位作者 张雷 张笠 蒲俊伟 李勇 肖佃师 李致远 马海川 《天然气地球科学》 CAS CSCD 北大核心 2026年第4期801-815,共15页
多元回归、经验公式及岩石物理模型等传统手段难以充分捕捉测井曲线与储层参数间复杂的非线性关系,导致页岩储层分级预测精度较低。以渝西地区深层五峰组—龙一1亚段页岩为研究对象,建立基于贝叶斯优化LightGBM算法的深层页岩储层类型... 多元回归、经验公式及岩石物理模型等传统手段难以充分捕捉测井曲线与储层参数间复杂的非线性关系,导致页岩储层分级预测精度较低。以渝西地区深层五峰组—龙一1亚段页岩为研究对象,建立基于贝叶斯优化LightGBM算法的深层页岩储层类型识别模型,并利用SHAP算法定量评估测井曲线重要性,最终将模型应用于靶区储层分级评价。结果表明:相较于回归方案,分类方案在深层页岩储层识别模型复杂度、计算效率和识别性能上均显著提升。采用分类方案建立页岩储层类型识别模型,测试集中,LightGBM对储层类型识别的加权精确率(Weighted‑P)和召回率(Weighted‑R)分别为89.7%和89.6%,优于RF(87.52%和86.96%)和SVM(83.61%和81.8%)算法;DEN、GR和CNL曲线对识别I类和III类页岩储层最为重要,而识别II类储层中,DEN、AC和CNL曲线重要性更高,且测井曲线对模型决策呈现复杂非线性影响;分级评价结果显示I类储层主要发育于五峰组上部及龙一1亚段1小层。基于贝叶斯优化LightGBM算法可实现深层页岩储层类型的高效精确识别,为深层页岩储层分级评价提供了新思路。 展开更多
关键词 深层页岩储层 分级评价 LightGBM算法 贝叶斯优化 SHAP算法
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华法林个体化给药预测模型性能评估:贝叶斯药代动力学模型与机器学习算法的对比分析 认领 引用
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作者 李嘉媛 张磊 +4 位作者 胡晓婷 王荣 兰蕊 冬颖 孙建军 《中南药学》 CAS 2026年第7期311-316,共6页
目的通过对比基于贝叶斯原理的华法林剂量计算器(WDC)与两种机器学习算法——分类与回归树(CART)及随机森林,评估其对华法林周剂量的预测准确性。方法收集2017年1月至2024年12月期间101例抗凝治疗患者的临床和基因型数据,随机划分为训练... 目的通过对比基于贝叶斯原理的华法林剂量计算器(WDC)与两种机器学习算法——分类与回归树(CART)及随机森林,评估其对华法林周剂量的预测准确性。方法收集2017年1月至2024年12月期间101例抗凝治疗患者的临床和基因型数据,随机划分为训练集(70例)和测试集(31例)。通过平均绝对误差(MAE)、均方误差(MSE)、均方根误差(RMSE)以及预测值在实际剂量±20%范围内的比例评估预测性能。目标国际标准化比值(INR)范围为2.0~3.0。结果随机森林模型具有最低的RMSE(4.75 mg/周)和最高的准确率(77.42%),其预测值在实际剂量±20%范围内的表现显著优于WDC和CART模型。结论在本研究队列中,机器学习算法中随机森林模型表现出最优的剂量预测性能。 展开更多
关键词 华法林 机器学习 贝叶斯算法 个体化医疗
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