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A comparative study on the prediction of the bp artificial neural network model and the ARIMA model in the incidence of AIDS

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2020年第1期20卷 143-143页

作者： Li, Zeming Li, Yanning Peking Univ Sch Publ Hlth 38 Xueyuan Rd Beijing 100191 Peoples R China Guangxi Med Univ Sch Publ Hlth 22 Shuangyong Rd Nanning 530021 Peoples R China

Background As a kind of widely distributed disease in China, acquired immune deficiency syndrome (AIDS) has been quickly growing each year, become a serious problem and caused serious damage to the life and health of people and the social events of China and the world because of its high fatality rate. It has been much concerned by all aspects of society. Therefore, developing early warning technology and finding the trend of early development are of quite significance to prevent and control human immunodeficiency virus (HIV)/AIDS. This study aimed to explore a suitable model for the morbidity of AIDS in China and establish a professional and feasible disease prediction model for the prevention and control works of AIDS. Methods At present, the traditional linear model is still utilized by most scholars to predict the incidence of HIV/AIDS. In addition, some scholars may attempt to use the nonlinear prediction model. Both prediction models showed good fitting and prediction effects. In China, the incidence of AIDS presents linear and nonlinear characteristics. In this research, the nonlinear back propagation artificial neural network (bp-ANN) model and the typical auto-regressive integrated moving average (ARIMA) linear model were applied to predict the incidence of HIV/AIDS and compare their fitting effects. Results Both models were capable of predicting the expected cases of AIDS. It was seen that ARIMA and bp-ANN models could be used to forecast the monthly incidence of HIV/AIDS, but the fitting and forecasting effects of the nonlinear bp neural network model were better than those of the traditional linear ARIMA model. Conclusions In summary, it was further concluded that the bp-ANN model was a suitable way to monitor and predict the change trend and morbidity of AIDS in China.

关键词： AIDS Prediction bp artificial neural network model ARIMA model

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Feasibility of measuring moisture content of green sand by a low frequency multiprobe detector based on dielectric characteristics

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China Foundry 2023年第3期20卷 197-206页

作者： De-quan Shi Gui-li Gao Ming Sun Ya-xin Huang School of Materials and Chemistry University of Shanghai for Science and TechnologyShanghai 200093China School of Mechanical and Energy Engineering Shanghai Technical Institute of Electronics and InformationShanghai 201411China School of Materials Science&Chemical Engineering Harbin University of Science and TechnologyHarbin 150040China

Green sand is a mixture of silica sand,bentonite,water and coal powder,and other *** content is an important index to characterize the properties of green *** on the dielectric characteristics of green sand and transmission line theory,a method for rapidly measuring the moisture content of green sand by means of a low frequency multiprobe detector was proposed.A system was constructed,where six detectors with different arrangements and probes were *** experimental results showed that the voltage difference of transmission line increases with the increasing frequency before 29 MHz while decreases after 35 MHz.A voltage difference platform occurs in the range of 29-35 MHz,which is suitable for measuring the moisture content due to its insensitivity to *** electric field intensity gradually decreases with the increase of the probe depth,and the intensity of central probe is always greater than that of the edge *** the distance of the probe away from the sand sample surface is 80 mm,the electric field intensity of the edge probe is found to be very *** optimal excitation frequency for measuring the moisture content of green sand is 29-33 *** optimal detector is the one with one center probe and three edge probes,and their lengths are 80 mm and 60 mm,*** distance between the center and edge probes is 25 mm,and the diameter of probes is 5 *** the voltage difference of transmission line,bentonite content,coal powder content and compactability as parameters of the input layer,and the moisture content as a parameter of the output layer,a three-layer bp artificial neural network model for predicting the moisture content of green sand was constructed according to the experimental results at 33 *** prediction error of the model is not higher than 3.3% when the moisture content of green sand is within the range of 3wt.%-7wt.%.

关键词： green sand dielectric property moisture content multiprobe detector bp artificial neural network model

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Comparison Study on Ways of Ecological Vulnerability Assessment - A Case Study in the Hengyang Basin

Comparison Study on Ways of Ecological Vulnerability Assessm...

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3rd International Conference on Environmental Science and Information Application Technology (ESIAT)

作者： Li, Yin Tian, Ya-ping Li, Chao-kui Hunan Univ Sci & Technol Inst Geospatial Informat Sci Xiangtan 411201 Peoples R China Hengyang Normal Univ Dept Resources Environm & Tourism Management Hengyang 421008 Peoples R China

In the paper, Authors separately used analytical hierarchy process, expert scoring method, principal component and bp network to establish the weights of ecological vulnerability evaluation Index system in the Hengyang Basin as a case, and used hierarchical-comprehensive index -clustering model, principal component-comprehensive index-clustering analysis model, expert scoring-composite index-clustering analysis model, bp artificial neural network model to evaluate separately the reality vulnerability in 2000. The results shows that study area are moderate weak, and the results broadly similar. Authors think that Principal component and artificial neural network model can avoid subjective factors, and taking the way of the expert group judgment is to reduce the error of ecological vulnerability assessment. With the impact of global change and the strengthening of fragile ecosystems, China has carried out many researches and practices on vulnerability assessments[1,14]. Although Vulnerability assessment in many ways, not yet formed a unified evaluation method, this paper draw on previous evaluation methods based on [1,11], using AHP, principal component analysis, bp neural network and expert scoring method on quantitative assessment and analysis, then clustering the results by comparison to determine which method to evaluate the most accurate. Authors analysis the Hengyang Basin reality vulnerability in 2000, attempting to explore a more suitable and easy erosion in hilly areas degraded fragile vulnerability assessment method. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Conference ESIAT2011 Organization Committee.

关键词： Ecological vulnerability Index system Hierarchical analysis Principal component analysis Clustering analysis bp artificial neural network model

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Study on Spatial Distribution of Soil Heavy Metals in Huizhou City Based on bp-ANN modeling and GIS

Study on Spatial Distribution of Soil Heavy Metals in Huizho...

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3rd International Conference on Environmental Science and Information Application Technology (ESIAT)

作者： Li, Yin Li, Chao-kui Jian-junTao Wang, Li-dong Hunan Univ Sci & Technol Inst Geospatial Informat Sci Xiangtan 411201 Peoples R China

Using bp neural network model and GIS for heavy metal descript the spatial dynamics of distribution in Huizhou City, Guangdong Province, The Results indicate:(1) bp neural network model can learn the relationship between spatial location of sampling points with content of them and be able to forecast soil heavy metal content. by interpolating more sampling points, such as Fig. 3 and Fig. 4, Cd from the original refuses the normal distribution to approximate submit to normal distribution, with the increase in sampling points, the data can be used in geostatistical analysis. the range of Cu was changed from the original 136.24km to 25.20 km, as TABLE 2. more realistic conditions.(2) Import the coordinates of the interpolation data and heavy metal content data into Arcgis, the most significant pollution is Pb, most of the area level over the natural background value. Zn, Cd have a high aggregation in some areas, the majority does not exceed the natural background value. Cu does not exceed the background, as shown in Figure 11 to 14. Therefore, the most noteworthy is the Pb pollution. Use the single factor index method, the four metal did not meet pollution threshold, indicating the study area have not been heavy metal pollution of soil.(3) Compared original data and interpolated data of the background index, as shown in Table 2 and Table 3, the nugget and sill ratio of Cu and Zn decreased, indicating that as interpolation points increase, Cu, Zn by the random factor decreases, increased spatial correlation, Pb nugget and sill ratio increases, the spatial correlation decreases, Cu by the random factors the strongest, spatial correlation the weakest, Zn and Pb is mainly affected by structural factors. Soil is not a homogeneous body, rather a variant of spatial continuity, this highly complex spatial heterogeneity of soil allows us to study the spatial dynamics of change becomes very difficult, caused not only by nature but also by human activities. These changes are the

关键词： bp artificial neural network model GIS technology Soil heavy metal Huizhou city Spatial distribution Spatial interpolation Geostatistics

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Comparison Study on Ways of Ecological Vulnerability Assessment ----- A Case Study in the Hengyang Basin

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Procedia Environmental Sciences 2011年 10卷 2067-2074页

作者： Yin Li Ya-ping Tian Chao-kui Li Institute of Geospatial Information Science Hunan University of Science and Technology Xiangtan 411201 China Department of Resources Environment and Tourism Management Hengyang Normal University Hengyang 421008 China

In the paper, Authors separately used analytical hierarchy process, expert scoring method, principal component and bp network to establish the weights of ecological vulnerability evaluation Index system in the Hengyang Basin as a case, and used hierarchical-comprehensive index -clustering model, principal component-comprehensive index- clustering analysis model, expert scoring-composite index-clustering analysis model, bp artificial neural network model to evaluate separately the reality vulnerability in 2000. The results shows that study area are moderate weak, and the results broadly similar. Authors think that Principal component and artificial neural network model can avoid subjective factors, and taking the way of the expert group judgment is to reduce the error of ecological vulnerability assessment. With the impact of global change and the strengthening of fragile ecosystems, China has carried out many researches and practices on vulnerability assessments [1,14].Although Vulnerability assessment in many ways, not yet formed a unified evaluation method, this paper draw on previous evaluation methods based on [1,11], using AHP, principal component analysis, bp neural network and expert scoring method on quantitative assessment and analysis, then clustering the results by comparison to determine which method to evaluate the most *** analysis the Hengyang Basin reality vulnerability in 2000,attempting to explore a more suitable and easy erosion in hilly areas degraded fragile vulnerability assessment method.

关键词： Ecological vulnerability Index system Hierarchical analysis Principal component analysis Clustering analysis bp artificial neural network model

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Study on Spatial Distribution of Soil Heavy Metals in Huizhou City Based on bp--ANN modeling and GIS

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Procedia Environmental Sciences 2011年 10卷 1953-1960页

作者： Yin Li Chao-kui Li Jian-jun Tao Li-dong Wang Institute of Geospatial Information Science Hunan University of Science and Technology Xiangtan 411201 China

Using bp neural network model and GIS for heavy metal descript the spatial dynamics of distribution in Huizhou City, Guangdong Province, The Results indicate:(1)bp neural network model can learn the relationship between spatial location of sampling points with content of them and be able to forecast soil heavy metal content. by interpolating more sampling points,such as Fig.3 and Fig.4, Cd from the original refuses the normal distribution to approximate submit to normal distribution, with the increase in sampling points, the data can be used in geostatistical analysis. the range of Cu was changed from the original 136.24 km to 25.20 km, as TABLE 2. more realistic conditions.(2)Import the coordinates of the interpolation data and heavy metal content data into Arcgis, the most significant pollution is Pb, most of the area level over the natural background value .Zn, Cd have a high aggregation in some areas, the majority does not exceed the natural background value. Cu does not exceed the background, as shown in Figure 11 to ***, the most noteworthy is the Pb pollution. Use the single factor index method, the four metal did not meet pollution threshold, indicating the study area have not been heavy metal pollution of soil.(3) Compared original data and interpolated data of the background index, as shown in Table 2 and Table 3, the nugget and sill ratio of Cu and Zn decreased, indicating that as interpolation points increase, Cu, Zn by the random factor decreases, increased spatial correlation, Pb nugget and sill ratio increases, the spatial correlation decreases, Cu by the random factors the strongest, spatial correlation the weakest, Zn and Pb is mainly affected by structural factors. Soil is not a homogeneous body, rather a variant of spatial continuity, this highly complex spatial heterogeneity of soil allows us to study the spatial dynamics of change becomes very difficult, caused not only by nature but also by human activities. These changes are the s

关键词： bp artificial neural network model GIS technology Soil heavy metal Huizhou city Spatial distribution Spatial interpolation Geostatistics

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Study on Spatial Distribution of Soil Heavy Metals in Huizhou City Based on bp--ANN modeling and GIS

Study on Spatial Distribution of Soil Heavy Metals in Huizho...

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2011 3rd International Conference on Environmental Science and Information Application Technology(ESIAT 2011)

作者： Yin Li Chao-kui Li Jian-junTao Li-dong Wang Institute of Geospatial Information Science Hunan University of Science and Technology

Using bp neural network model and GIS for heavy metal descript the spatial dynamics of distribution in Huizhou City, Guangdong Province, The Results indicate:(1)bp neural network model can learn the relationship between spatial location of sampling points with content of them and be able to forecast soil heavy metal content. by interpolating more sampling points,such as Fig.3 and Fig.4, Cd from the original refuses the normal distribution to approximate submit to normal distribution, with the increase in sampling points, the data can be used in geostatistical analysis. the range of Cu was changed from the original 136.24 km to 25.20 km, as TABLE 2. more realistic conditions.(2)Import the coordinates of the interpolation data and heavy metal content data into Arcgis, the most significant pollution is Pb, most of the area level over the natural background ***, Cd have a high aggregation in some areas, the majority does not exceed the natural background value. Cu does not exceed the background, as shown in Figure 11 to ***, the most noteworthy is the Pb pollution. Use the single factor index method, the four metal did not meet pollution threshold, indicating the study area have not been heavy metal pollution of soil.(3) Compared original data and interpolated data of the background index, as shown in Table 2 and Table 3, the nugget and sill ratio of Cu and Zn decreased, indicating that as interpolation points increase, Cu, Zn by the random factor decreases, increased spatial correlation, Pb nugget and sill ratio increases, the spatial correlation decreases, Cu by the random factors the strongest, spatial correlation the weakest, Zn and Pb is mainly affected by structural factors. Soil is not a homogeneous body, rather a variant of spatial continuity, this highly complex spatial heterogeneity of soil allows us to study the spatial dynamics of change becomes very difficult, caused not only by nature but also by human activities. These changes are the spatia

关键词： bp artificial neural network model GIS technology Soil heavy metal Huizhou city Spatial distribution Spatial interpolation Geostatistics

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Comparison Study on Ways of Ecological Vulnerability Assessment——A Case Study in the Hengyang Basin

Comparison Study on Ways of Ecological Vulnerability Assessm...

引用

2011 3rd International Conference on Environmental Science and Information Application Technology(ESIAT 2011)

作者： Yin Li Ya-ping Tian Chao-kui Li Institute of Geospatial Information Science Hunan University of Science and Technology Department of Resources Environment and Tourism Management Hengyang Normal University

In the paper, Authors separately used analytical hierarchy process, expert scoring method, principal component and bp network to establish the weights of ecological vulnerability evaluation Index system in the Hengyang Basin as a case, and used hierarchical-comprehensive index-clustering model, principal component-comprehensive indexclustering analysis model, expert scoring-composite index-clustering analysis model, bp artificial neural network model to evaluate separately the reality vulnerability in 2000. The results shows that study area are moderate weak, and the results broadly similar. Authors think that Principal component and artificial neural network model can avoid subjective factors, and taking the way of the expert group judgment is to reduce the error of ecological vulnerability assessment. With the impact of global change and the strengthening of fragile ecosystems, China has carried out many researches and practices on vulnerability assessments[1,14].Although Vulnerability assessment in many ways, not yet formed a unified evaluation method, this paper draw on previous evaluation methods based on [1,11], using AHP, principal component analysis, bp neural network and expert scoring method on quantitative assessment and analysis, then clustering the results by comparison to determine which method to evaluate the most *** analysis the Hengyang Basin reality vulnerability in 2000,attempting to explore a more suitable and easy erosion in hilly areas degraded fragile vulnerability assessment method.

关键词： Ecological vulnerability Index system Hierarchical analysis Principal component analysis Clustering analysis bp artificial neural network model

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Forecasting the Natural Forest Stand Age Based on artificial neural network model

Forecasting the Natural Forest Stand Age Based on Artificial...

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2010 International Conference on Computer and Communication Technologies in Agriculture Engineering (CCTAE 2010)

作者： NING Yang-cui,ZHENG Xiao-xian,ZHAO Jing,JIANG gui-juan The Key Laboratory for Silviculture and Conservation of Ministry of Education Beijing Forestry University Beijing,China

Stand age is the average age of trees in the *** this paper,the back propagation (bp) artificial neural network,the projection pursuit regression (PPR) artificial neural network and the multiple stepwise regression anatomic models were introduced to predict the nonlinear relation between the natural stand age and the stand *** the same time,the author contrasted the forecasting results precision of the bp artificial neural network model,the PPR artificial neural network and the multiple stepwise regression anatomic *** result indicated that 3 models were available for prediction of natural forest age;the predication average relative error of bp artificial neural network model was 0.1,he predication average relative error of multiple stepwise regression anatomic models was 0.05,the predication average relative error of PPR artificial neural network model was 0.02;the stability of bp artificial neural network model was poor and in other hand the PPR model and the multiple stepwise regression anatomic model with good ***,the PPR model was better than the other two models,which can be applied to predict the natural forest stand age.

关键词： bp artificial neural network model PPR artificial neural network mode multiple stepwise regression anatomic models stands age

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modeling of Atrazine Adsorption onto Surficial Sediments in the System of Cadmium and Malathion Co-existed

Modeling of Atrazine Adsorption onto Surficial Sediments in ...

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4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE)

作者： Wang, Zhizeng Gao, Qian Hu, Yan Li, Yu North China Elect Power Univ Energy & Environm Res Ctr Beijing Peoples R China

ISBN: (纸本)9781424447138

Based on the data of single atrazine (AT) adsorption, co-sorption of AT and cadmium (Cd), and co-sorption of AT and malathion (Ma) onto surficial sediments (SSs), a bp artificial neural network (ANN) model was established, in which the inputs were selected as concentrations of AT, Cd, Ma and the output was the amount of AT adsorption onto SSs, in order to simulate the interaction of co-existed Ma and Cd on AT adsorption. The results verified that the model could simulate the effect of Ma, Cd on the adsorption of AT onto SSs, there was only less than 8.19% of average relative error existed between the values predicated through the bp model and experimental ones. The determination coefficient between the fitting curve and the Nash-Sutcliffe simulation efficiency coefficient (NSC) was 0.9973 (> 0.80), indicating the model could describe AT adsorption onto SSs in the system of Cd and Ma co-existed well. The influence of Cd and Ma on the adsorption of AT in the ATCd- Ma system could be also predicted via the established bp ANN model. The results show that the presence of Cd in the ATCd- Ma system will enhance the adsorption of AT and the higher Cd concentration, the less AT will be adsorbed onto SSs;moreover, the co-existed Ma will restrain the adsorption of AT due to competitive sorption behavior.

关键词： atrazine cadmium malathion co-contamination bp artificial neural network model

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