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
This paper develops a joint model utilizing the principal component analysis(PCA)and the back propagation(BP)neural network model optimized by the Levenberg Marquardt(LM)algorithm,and as an application of the joint mo...This paper develops a joint model utilizing the principal component analysis(PCA)and the back propagation(BP)neural network model optimized by the Levenberg Marquardt(LM)algorithm,and as an application of the joint model to investigate the damages caused by typhoons for a coastal province,Fujian Province,China in 2005-2015(latest).First,the PCA is applied to analyze comprehensively the relationship between hazard factors,hazard bearing factors and disaster factors.Then five integrated indices,overall disaster level,typhoon intensity,damaged condition of houses,medical rescue and self-rescue capability,are extracted through the PCA;Finally,the BP neural network model,which takes the principal component scores as input and is optimized by the LM algorithm,is implemented to forecast the comprehensive loss of typhoons.It is estimated that an average annual loss of 138.514 billion RMB occurred for 2005-2015,with a maximum loss of 215.582 in 2006 and a decreasing trend since 2010 though the typhoon intensity increases.The model was validated using three typhoon events and it is found that the error is less than 1%.These results provide information for the government to increase medical institutions and medical workers and for the communities to promote residents’self-rescue capability.展开更多
In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level...In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level model of this type with ixj=3x2,k=l,and the 1980 monthly mean temperture predichon on a long-t6rm basis were prepared by steadily modifying the weighting coefficient,making for the correlation coefficient of 97% with the measurements.Furthermore,the weighhng parameter was modified for each month of 1980 by means of observations,therefore constrcuhng monthly mean temperature forecasts from January to December of the year,reaching the correlation of 99.9% with the measurements.Likewise,the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlahon of 98% and the month-tO month forecasts of 99.4%.展开更多
Recent years we have witnessed the rapid growth of social commerce in China, but many users are not willing to trust and use social commerce. So improving consumers’ trust and purchase intention has become a crucial ...Recent years we have witnessed the rapid growth of social commerce in China, but many users are not willing to trust and use social commerce. So improving consumers’ trust and purchase intention has become a crucial factor in the success of social commerce. Business factors, environment factors and social factors including twelve secondary indexes build up a social commerce trust evaluation model. Questionnaires are handed out to collect twelve secondary indexes scores as input of BP neural network and composite score of trust as output. Model simulation shows that both training samples and test samples have low level of average error and standard deviation, which certify that the model has good stability and it is a good method for evaluating social commerce trust.展开更多
By using alternating current plasma arc welding,the influences were studied of such parameters as welding curent,arc voltage,welding speed,wire feed rate,and magnitude of ion gas flow on front melting width,wdle rei...By using alternating current plasma arc welding,the influences were studied of such parameters as welding curent,arc voltage,welding speed,wire feed rate,and magnitude of ion gas flow on front melting width,wdle reinforcement,and back melting width of LF6 aluminum alloy.Model of the formation of welding seam in alternating current plasma arc welding of aluminum was set up with the method of artificial neural neural network - BP algorithm. Qyakuty of formation was consequently predicted and evaluated.The experimental result shows that,compared with other modeling methods,artificial network model can be used to more accurately predict formation of weld,and to guide the production practice.展开更多
The HCl emission characteristics of typical municipal solid waste(MSW) components and their mixtures have been investigated in a Φ150 mm fluidized bed. Some influencing factors of HCl emission in MSW fluidized bed in...The HCl emission characteristics of typical municipal solid waste(MSW) components and their mixtures have been investigated in a Φ150 mm fluidized bed. Some influencing factors of HCl emission in MSW fluidized bed incinerator was found in this study. The HCl emission is increasing with the growth of bed temperature, while it is decreasing with the increment of oxygen concentration at furnace exit. When the weight percentage of auxiliary coal is increased, the conversion rate of Cl to HCl is increasing. The HCl emission is decreased, if the sorbent(CaO) is added during the incineration process. Based on these experimental results, a 14×6×1 three-layer BP neural networks prediction model of HCl emission in MSW/coal co-fired fluidized bed incinerator was built. The numbers of input nodes and hidden nodes were fixed on by canonical correlation analysis technique and dynamic construction method respectively. The prediction results of this model gave good agreement with the experimental results, which indicates that the model has relatively high accuracy and good generalization ability. It was found that BP neural network is an effectual method used to predict the HCl emission of MSW/coal co-fired fluidized bed incinerator.展开更多
Based on the basic principles of BP artificial neural network model and the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall co...Based on the basic principles of BP artificial neural network model and the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall conditions serving as affecting factors. The model has satisfactory performance of learning and generalization and can be also used to assess the influence of human activities on water and sediment yield in a river basin. The model is applied to compute the runoff and sediment transmission at Xingshan, Bixi and Shunlixia stations. Comparison between the results from the model and the observed data shows that the model is basically reasonable and reliable.展开更多
Evaluation of ecological carrying capacity is an important method of analyzing regional sustainable development, study on ecological carrying capacity is to settle the contradictions between resource and environment, ...Evaluation of ecological carrying capacity is an important method of analyzing regional sustainable development, study on ecological carrying capacity is to settle the contradictions between resource and environment, and it is a significant basis for realizing regional sustainable development. This paper, on the basis of the academician Sun Tiehang's "unification of three" for the eco-city construction, established ecological carrying capacity evaluation indexes for the traditional industrial and mining city—Huainan City; and applied GM–BP neural network coupling model for the dynamic evolution and prediction of ecological carrying capacity of Huainan City in the future decade. The results showed that ecological carrying capacity index of Huainan would be 2.13 by 2025, higher than the loadable state 1, so the ecological carrying capacity would keep in the over-loaded level, but the over-loaded degree would be lower than the current. Carrying capacity of arable land, energy and water resources contribute greatly to the improvement of ecological carrying capacity, thus it is imperative to adjust this unreasonable and unsustainable ecological consumption relationship, enhance environmental protection awareness and high-efficiency utilization of resources, and take an energy-saving and intensive development path.展开更多
To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are anal...To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.展开更多
In order to effectively predict occurrence quantity of Myzus persicae, BP neural network theory and method was used to establish prediction model for oc- currence quantity of M. persicae. Meanwhile, QPSO algorithm was...In order to effectively predict occurrence quantity of Myzus persicae, BP neural network theory and method was used to establish prediction model for oc- currence quantity of M. persicae. Meanwhile, QPSO algorithm was used to optimize connection weight and threshold value of BP neural network, so as to determine. the optimal connection weight and threshold value. The historical data of M. persica quantity in Hongta County, Yuxi City of Yunnan Province from 2003 to 2006 was adopted as training samples, and the occurrence quantities of M. persicae from 2007 to 2009 were predicted. The prediction accuracy was 99.35%, the mini- mum completion time was 30 s, the average completion time was 34.5 s, and the running times were 19. The prediction effect of the model was obviously superior to other prediction models. The experiment showed that this model was more effective and feasible, with faster convergence rate and stronger stability, and could solve the similar problems in prediction and clustering. The study provides a theoretical basis for comprehensive prevention and control against M. persicae.展开更多
文摘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.
文摘This paper develops a joint model utilizing the principal component analysis(PCA)and the back propagation(BP)neural network model optimized by the Levenberg Marquardt(LM)algorithm,and as an application of the joint model to investigate the damages caused by typhoons for a coastal province,Fujian Province,China in 2005-2015(latest).First,the PCA is applied to analyze comprehensively the relationship between hazard factors,hazard bearing factors and disaster factors.Then five integrated indices,overall disaster level,typhoon intensity,damaged condition of houses,medical rescue and self-rescue capability,are extracted through the PCA;Finally,the BP neural network model,which takes the principal component scores as input and is optimized by the LM algorithm,is implemented to forecast the comprehensive loss of typhoons.It is estimated that an average annual loss of 138.514 billion RMB occurred for 2005-2015,with a maximum loss of 215.582 in 2006 and a decreasing trend since 2010 though the typhoon intensity increases.The model was validated using three typhoon events and it is found that the error is less than 1%.These results provide information for the government to increase medical institutions and medical workers and for the communities to promote residents’self-rescue capability.
文摘In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level model of this type with ixj=3x2,k=l,and the 1980 monthly mean temperture predichon on a long-t6rm basis were prepared by steadily modifying the weighting coefficient,making for the correlation coefficient of 97% with the measurements.Furthermore,the weighhng parameter was modified for each month of 1980 by means of observations,therefore constrcuhng monthly mean temperature forecasts from January to December of the year,reaching the correlation of 99.9% with the measurements.Likewise,the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlahon of 98% and the month-tO month forecasts of 99.4%.
文摘Recent years we have witnessed the rapid growth of social commerce in China, but many users are not willing to trust and use social commerce. So improving consumers’ trust and purchase intention has become a crucial factor in the success of social commerce. Business factors, environment factors and social factors including twelve secondary indexes build up a social commerce trust evaluation model. Questionnaires are handed out to collect twelve secondary indexes scores as input of BP neural network and composite score of trust as output. Model simulation shows that both training samples and test samples have low level of average error and standard deviation, which certify that the model has good stability and it is a good method for evaluating social commerce trust.
文摘By using alternating current plasma arc welding,the influences were studied of such parameters as welding curent,arc voltage,welding speed,wire feed rate,and magnitude of ion gas flow on front melting width,wdle reinforcement,and back melting width of LF6 aluminum alloy.Model of the formation of welding seam in alternating current plasma arc welding of aluminum was set up with the method of artificial neural neural network - BP algorithm. Qyakuty of formation was consequently predicted and evaluated.The experimental result shows that,compared with other modeling methods,artificial network model can be used to more accurately predict formation of weld,and to guide the production practice.
文摘The HCl emission characteristics of typical municipal solid waste(MSW) components and their mixtures have been investigated in a Φ150 mm fluidized bed. Some influencing factors of HCl emission in MSW fluidized bed incinerator was found in this study. The HCl emission is increasing with the growth of bed temperature, while it is decreasing with the increment of oxygen concentration at furnace exit. When the weight percentage of auxiliary coal is increased, the conversion rate of Cl to HCl is increasing. The HCl emission is decreased, if the sorbent(CaO) is added during the incineration process. Based on these experimental results, a 14×6×1 three-layer BP neural networks prediction model of HCl emission in MSW/coal co-fired fluidized bed incinerator was built. The numbers of input nodes and hidden nodes were fixed on by canonical correlation analysis technique and dynamic construction method respectively. The prediction results of this model gave good agreement with the experimental results, which indicates that the model has relatively high accuracy and good generalization ability. It was found that BP neural network is an effectual method used to predict the HCl emission of MSW/coal co-fired fluidized bed incinerator.
文摘Based on the basic principles of BP artificial neural network model and the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall conditions serving as affecting factors. The model has satisfactory performance of learning and generalization and can be also used to assess the influence of human activities on water and sediment yield in a river basin. The model is applied to compute the runoff and sediment transmission at Xingshan, Bixi and Shunlixia stations. Comparison between the results from the model and the observed data shows that the model is basically reasonable and reliable.
基金Sponsored by National Natural Science Foundation of China(41101566)
文摘Evaluation of ecological carrying capacity is an important method of analyzing regional sustainable development, study on ecological carrying capacity is to settle the contradictions between resource and environment, and it is a significant basis for realizing regional sustainable development. This paper, on the basis of the academician Sun Tiehang's "unification of three" for the eco-city construction, established ecological carrying capacity evaluation indexes for the traditional industrial and mining city—Huainan City; and applied GM–BP neural network coupling model for the dynamic evolution and prediction of ecological carrying capacity of Huainan City in the future decade. The results showed that ecological carrying capacity index of Huainan would be 2.13 by 2025, higher than the loadable state 1, so the ecological carrying capacity would keep in the over-loaded level, but the over-loaded degree would be lower than the current. Carrying capacity of arable land, energy and water resources contribute greatly to the improvement of ecological carrying capacity, thus it is imperative to adjust this unreasonable and unsustainable ecological consumption relationship, enhance environmental protection awareness and high-efficiency utilization of resources, and take an energy-saving and intensive development path.
文摘To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.
基金Supported by Science and Technology Project of China National Tobacco Corporation(2009YN005&2010YN18&2010YN19)
文摘In order to effectively predict occurrence quantity of Myzus persicae, BP neural network theory and method was used to establish prediction model for oc- currence quantity of M. persicae. Meanwhile, QPSO algorithm was used to optimize connection weight and threshold value of BP neural network, so as to determine. the optimal connection weight and threshold value. The historical data of M. persica quantity in Hongta County, Yuxi City of Yunnan Province from 2003 to 2006 was adopted as training samples, and the occurrence quantities of M. persicae from 2007 to 2009 were predicted. The prediction accuracy was 99.35%, the mini- mum completion time was 30 s, the average completion time was 34.5 s, and the running times were 19. The prediction effect of the model was obviously superior to other prediction models. The experiment showed that this model was more effective and feasible, with faster convergence rate and stronger stability, and could solve the similar problems in prediction and clustering. The study provides a theoretical basis for comprehensive prevention and control against M. persicae.
