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Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data—A case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake 认领 引用 被引量：6
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作者 XU Min ZENG Guang-ming +3 位作者 XU Xin-yi HUANG Guo-he SUN Wei JIANG Xiao-yun 《Journal of Environmental Sciences》 SCIE EI CAS 2005年第6期946-952,共7页
Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of t... Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake. 展开更多
关键词 Dongting Lake chlorophyll-a Bayesian regularized BP neural network model sum of square weights
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Ontology Mapping Based on Bayesian Network 认领 引用 被引量：2
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作者 张凌宇 陶佰睿 《Journal of Donghua University(English Edition)》 EI CAS 2015年第4期681-687,共7页
Ontology mapping is a key interoperability enabler for the semantic web. In this paper,a new ontology mapping approach called ontology mapping based on Bayesian network( OM-BN) is proposed. OM-BN combines the models o... Ontology mapping is a key interoperability enabler for the semantic web. In this paper,a new ontology mapping approach called ontology mapping based on Bayesian network( OM-BN) is proposed. OM-BN combines the models of ontology and Bayesian Network,and applies the method of Multi-strategy to computing similarity. In OM-BN,the characteristics of ontology,such as tree structure and semantic inclusion relations among concepts,are used during the process of translation from ontology to ontology Bayesian network( OBN). Then the method of Multi-strategy is used to create similarity table( ST) for each concept-node in OBN. Finally,the iterative process of mapping reasoning is used to deduce new mappings from STs,repeatedly. 展开更多
关键词 component ontology mapping multi-strategy Bayesian network model
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Hierarchy Bayesian model based services awareness of high-speed optical access networks 认领 引用
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作者 白晖峰 《Optoelectronics Letters》 EI 2018年第2期114-118,共5页
As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve t... As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve this problem, a hierarchy Bayesian model based services awareness mechanism is proposed for high-speed optical access networks. This approach builds a so-called hierarchy Bayesian model, according to the structure of typical optical access networks. Moreover, the proposed scheme is able to conduct simple services awareness operation in each optical network unit(ONU) and to perform complex services awareness from the whole view of system in optical line terminal(OLT). Simulation results show that the proposed scheme is able to achieve better quality of services(Qo S), in terms of packet loss rate and time delay. 展开更多
关键词 As Simulation OLT Hierarchy Bayesian model based services awareness of high-speed optical access networks
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Building Bayesian Network(BN)-Based System Reliability Model by Dual Genetic Algorithm(DGA) 认领 引用
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作者 游威振 钟小品 《Journal of Donghua University(English Edition)》 EI CAS 2015年第6期914-918,共5页
A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In con... A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In contrast with traditional methods where BN model is built by professionals,DGA is proposed for the automatic analysis of historical data and construction of BN for the estimation of system reliability.The whole solution space of BN structures is searched by DGA and a more accurate BN model is obtained.Efficacy of the proposed method is shown by some literature examples. 展开更多
关键词 Bayesian network(BN)model dual genetic algorithm(DGA) system reliability historical data
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Predicting the nephrotoxicity of Chinese herbal medicines based on a Bayesian network model 认领 引用
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作者 Li-Juan Tan Liang Chen +2 位作者 Jia-Hui Huang Ze-Hai Fang Hong-Jie Liu 《TMR Pharmacology Research》 2022年第1期22-29,共8页
Objective:Based on a Bayesian network model(BNM),we constructed and evaluated a predictive model of Chinese herbal medicines(CHMs)nephrotoxicity,explored its influencing factors,and provided a reference for the preven... Objective:Based on a Bayesian network model(BNM),we constructed and evaluated a predictive model of Chinese herbal medicines(CHMs)nephrotoxicity,explored its influencing factors,and provided a reference for the prevention and control of nephrotoxicity.Methods:We searched for CHMs with nephrotoxicity through academic journals and academic works,screened non-nephrotoxic CHMs,and then tested the correlation between nephrotoxic and non-nephrotoxic CHMs and their four properties,five flavours,and channel tropism.The screened variables were used to construct the Bayesian network model(BNM),predict important factors affecting the nephrotoxicity of Chinese herbal medicines(CHMs),draw the receiver operating characteristic(ROC)curve of the model,and calculate the area under the curve(AUC)to evaluate the forecasting effect of the model.Results:Medicinal property theory(four properties and five flavours)are important factors affecting the nephrotoxicity of CHMs.Nephrotoxic and non-nephrotoxic CHMs are related to their four propertiesand five flavours(P<0.05).BNM showed that sweetness and flatness wereimportant protective factors for nephrotoxicity of CHMs;the prediction accuracy was 77.92%,the AUC result of the model ROC curve was 0.661(95%CI:0.620-0.701),and the best sensitivity(0.736)and specificity(0.571)were obtained at 0.65.Discussion:Modern mathematical statistics and modeling methods have certain reference significance and application value for the prediction of CHMs nephrotoxicity and toxicology research. 展开更多
关键词 Chinese herbal medicines four properties five flavours channel tropism prediction of nephrotoxicity Bayesian network model
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Bayesian Network Model of Product Information Diffusion and Reasoning of Influence 认领 引用
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作者 Xuehua Sun Shaojie Hou +2 位作者 Ning Cai Wenxiu Ma Surui Zhao 《Journal of Data Analysis and Information Processing》 2020年第4期267-281,共15页
Information diffusion on social media has become a key strategy in people’s daily interactions. This paper studies consumers’ participation in the product information diffusion, and analyzes the complexity of inform... Information diffusion on social media has become a key strategy in people’s daily interactions. This paper studies consumers’ participation in the product information diffusion, and analyzes the complexity of information diffusion which is affected by many factors. Prior investigations of information diffusion have primarily focused on the composition of diffusion networks with independent factors and the intricacy of the process has not been completely evaluated. The majority of prior investigations have focused on strategies and the moving forces in social media processes and the determination of influential seed nodes, with few evaluations conducted about the factors affecting consumers’ choices in information diffusion. In this study, a Bayesian network model of product information diffusion was created to examine the links between factors and consumer deportment. It revealed how those factors had an impact on each other and on consumer deportment choice. The innovation of the thesis is reflected in the exploration and analysis of the specific communication path of product information diffusion, which provides a better marketing idea and practical method for the development of mobile e-commerce. The research findings can help identify the quantitative relationships between the factors affecting the process of product information diffusion and user behavior. 展开更多
关键词 Product Information Diffusion Bayesian Network Model Influence Reasoning Consumer Behaviors Clique Tree
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Analysis method for evaluating uncertainty in the machining process of thin-walled parts 认领 引用
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作者 Xiaoyue Li Zhaoze Sun +2 位作者 Hao Qi Yue Guo Shuowei Bai 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第3期136-147,共12页
The machining process of thin-walled components is full of many uncertainties,resulting in problems such as high batch inconsistency and low pass rate.In this paper,the Bayesian network uncertainty inference model of ... The machining process of thin-walled components is full of many uncertainties,resulting in problems such as high batch inconsistency and low pass rate.In this paper,the Bayesian network uncertainty inference model of machining process is constructed.The influence mechanism of input variables on machining distortion un-certainty is clarified.Initially,the uncertain variables are collected and the inference model of machining dis-tortion is constructed based on root-branch-leaf Bayesian network structure.The weight of the influence of each input variable on machining distortion uncertainty and maximum influence path are obtained.Next,an inverse Bayesian network is established,with the uncertainty inference results used as prior information to carry out inverse inference of machining distortion uncertainty.The influence possibility of the related factors of the main influence variables on the uncertainty of machining distortion is obtained.Aviation aluminum alloy T-shaped part was taken as an example,and the influence mechanism of initial residual stress,surface residual stress,cutting force and their related factors on the machining distortion uncertainty was investigated.The results indicated that the influential weights of initial residual stress,surface residual stress and cutting force on the machining distortion uncertainty was 0.33(maximum),0.23 and 0.04 respectively.The factors related to initial residual stress,surface residual stress,and cutting force had influence weights of 0.099(maximum),0.009,and 0.001 on the machining distortion uncertainty.The probabilities of the effects were 0.920,0.075 and 0.005,respectively.Finally,the paper compares the proposed model with MC-GBRT and BiLSTM-UP,showing average improvements of 46.7%and 63.1%in stability and 35.7%and 20.6%in computational speed,respectively.This paper proposes a Bayesian network-based machining distortion uncertainty inference model that effectively reveals the mapping mechanism between initial residual stress,surface residual stress,cutting force,and the machining distortion uncertainty in thin-walled components. 展开更多
关键词 Thin-walled components Machining distortion Uncertainty Bayesian network inference model Inverse Bayesian inference
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Predicting nitrogen surplus in agricultural lands of China using a hybrid machine learning approach with smaller datasets and fewer features 认领 引用
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作者 Hao Wang Gaofei Yin +8 位作者 Hongda Wen Feng Wang Ziwei Yang Xueying Sun Xulin Zhang Huiqing Jiao Mengyu Zhai Wenchao Li Hongbin Liu 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第4期743-753,共11页
Global nitrogen pollution in agricultural lands poses a major environmental challenge,complicating both assessment and mitigation of nitrogen surplus.Nitrogen surplus(NS)is a critical indicator for evaluating nitrogen... Global nitrogen pollution in agricultural lands poses a major environmental challenge,complicating both assessment and mitigation of nitrogen surplus.Nitrogen surplus(NS)is a critical indicator for evaluating nitrogen use efficiency.However,traditional NS estimation methods often require extensive data,which are difficult to obtain in data-scarce regions.In this study,a BN-ML NS prediction model was developed by coupling Bayesian Networks(BN)and Machine Learning(ML)using long-term monitoring data(including climate,soil,and crops)from 2000 to 2019 in China.The key findings are as follows:Nitrogen fertilizer application rate(NR)is the dominant factor influencing NS across the seven regions;however,due to differences in climate,cropping patterns,and soil types,the impact of NR exhibits spatial heterogeneity;The BN-ML model demonstrates strong predictive performance,with R2values exceeding 0.9;compared to traditional NS prediction models,the BN-ML model maintains high accuracy using only half the number of features(e.g.,NR,Yield)and fewer than 120 data points;when validated at the small watershed scale,the model achieved over 80%prediction accuracy.By enabling reliable NS prediction with limited data,the proposed model supports more targeted and sustainable nitrogen management.It can assist national and regional authorities in identifying high-risk areas,optimizing fertilizer use,and formulating region-specific agricultural strategies. 展开更多
关键词 Bayesian network model Extreme gradient boosting Nitrogen management
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Land-sea integrated suitability evaluation of underground space based on Pythagorean fuzzy Bayesian network 认领 引用 被引量：2
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作者 Xinyu Liu Hongjun Liu +3 位作者 Jie Dong Peng Yu Honghua Liu Guanghua Cheng 《Underground Space》 SCIE EI CSCD 2025年第5期197-215,共19页
Scientific development and utilization of urban underground space is an inevitable choice for sustainable urban development.However,in the previous suitability evaluation of underground space in coastal cities,the dev... Scientific development and utilization of urban underground space is an inevitable choice for sustainable urban development.However,in the previous suitability evaluation of underground space in coastal cities,the development potential of underground space in the sea area is not considered.Therefore,this study takes the coastal zone of Jiaozhou bay as the study area,establishes evaluation index systems for land and sea areas separately,and explores a new model for evaluating the suitability of underground space in coastal cities by integrating land and sea.In addition,an underground space suitability evaluation model based on the integration of Pythagorean fuzzy sets and Bayesian network is proposed.Firstly,the Pythagorean triangular fuzzy numbers are used to expand the fuzzy range of expert opinions.Then the Pythagorean triangular fuzzy hybrid geometric operator is used to realize the aggregation of expert opinions to solve the difficulty of obtaining the node conditional probability table by the traditional Bayesian network model of underground space suitability evaluation.Finally,the Pythagorean fuzzy Bayesian network is used as an evaluation tool to carry out the underground space suitability evaluation.Based on the evaluation result and urban planning,the overall planning and functional zoning guidelines for underground space development in the study area are given and the suitability and engineering construction difficulty analysis on the second subsea tunnel of Jiaozhou bay is conducted.The research results can provide a valuable reference for the coastal city planning department to develop and utilize underground space. 展开更多
关键词 Underground space Land-sea integration Pythagorean fuzzy sets Suitability evaluation Bayesian network model
Improving herdsmen’s well-being through scenario planning:A case study in Xilinhot City,Inner Mongolia Autonomous Region 认领 引用 被引量：2
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作者 Jiajia Liu Ganlin Huang +1 位作者 Peng Jia Liyuan Chen 《Geography and Sustainability》 2020年第3期181-188,共8页
Grassland ecosystems support well-being with food,shelter,income,and culture of herdsmen.While the associa-tion between ecosystem services and human well-being has been widely studied,such association is understudied ... Grassland ecosystems support well-being with food,shelter,income,and culture of herdsmen.While the associa-tion between ecosystem services and human well-being has been widely studied,such association is understudied in grassland ecosystems.This study aims to fill this gap through a case study of Xilinhot City,Inner Mongolia Autonomous Region,China.We examined the association between grassland provisioning services and herds-men’s well-being between 1985 and 2015 through participatory observations,interviews,surveys,and Bayesian belief network modeling.Considering the uncertainties of weather and sheep prices,we developed four scenarios to examine the future well-being of herdsmen.Our results show that the most important factor for herdsmen’s well-being was income,which is highly sensitive to the market price of sheep and precipitation.Considering the uncertainties of sheep prices and precipitation,scenario analysis revealed a divergence between income and well-being.While herdsmen’s income is most likely to increase with low precipitation and increased sheep prices,their well-being is most likely to improve with abundant precipitation and increased sheep prices.Based on our find-ings,we argue that developing alternative income sources(e.g.,tourism),reducing dependence on government subsidies through commercial insurance,and branding lamb with grassland ecosystem to alleviate the impact of price fluctuations would help improve herdsmen’s well-being in all scenarios. 展开更多
关键词 Grassland Ecosystem service Well-being Bayesian belief network model Scenario planning Inner Mongolia
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An Information Fusion Model of Innovation Alliances Based on the Bayesian Network 认领 引用 被引量：3
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作者 Jun Xia Yuqiang Feng +1 位作者 Luning Liu Dongjun Liu 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2018年第3期347-356,共10页
To solve the problem of information fusion from multiple sources in innovation alliances, an information fusion model based on the Bayesian network is presented. The multi-source information fusion process of innovati... To solve the problem of information fusion from multiple sources in innovation alliances, an information fusion model based on the Bayesian network is presented. The multi-source information fusion process of innovation alliances was classified into three layers, namely, the information perception layer, the feature clustering layer,and the decision fusion layer. The agencies in the alliance were defined as sensors through which information is perceived and obtained, and the features were clustered. Finally, various types of information were fused by the innovation alliance based on the fusion algorithm to achieve complete and comprehensive information. The model was applied to a study on economic information prediction, where the accuracy of the fusion results was higher than that from a single source and the errors obtained were also smaller with the MPE less than 3%, which demonstrates the proposed fusion method is more effective and reasonable. This study provides a reasonable basis for decision-making of innovation alliances. 展开更多
关键词 information fusion innovation alliance Bayesian networks forecasting model decision making big data
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