Accurate mapping of soil salinity and recognition of its influencing factors are essential for sustainable crop production and soil health. Although the influencing factors have been used to improve the mapping accura...Accurate mapping of soil salinity and recognition of its influencing factors are essential for sustainable crop production and soil health. Although the influencing factors have been used to improve the mapping accuracy of soil salinity, few studies have considered both aspects of spatial variation caused by the influencing factors and spatial autocorrelations for mapping. The objective of this study was to demonstrate that the ordinary kriging combined with back-propagation network(OK_BP), considering the two aspects of spatial variation, which can benefit the improvement of the mapping accuracy of soil salinity. To test the effectiveness of this approach, 70 sites were sampled at two depths(0–30 and 30–50 cm) in Ningxia Hui Autonomous Region, China. Ordinary kriging(OK), back-propagation network(BP) and regression kriging(RK) were used in comparison analysis; the root mean square error(RMSE), relative improvement(RI) and the decrease in estimation imprecision(DIP) were used to judge the mapping quality. Results showed that OK_BP avoided the both underestimation and overestimation of the higher and lower values of interpolation surfaces. OK_BP revealed more details of the spatial variation responding to influencing factors, and provided more flexibility for incorporating various correlated factors in the mapping. Moreover, OK_BP obtained better results with respect to the reference methods(i.e., OK, BP, and RK) in terms of the lowest RMSE, the highest RI and DIP. Thus, it is concluded that OK_BP is an effective method for mapping soil salinity with a high accuracy.展开更多
The joint roughness coefficient(JRC)is a key parameter in the assessment of mechanical properties and the stability of rock masses.This paper presents a novel approach to JRC evaluation using a genetic algorithm-optim...The joint roughness coefficient(JRC)is a key parameter in the assessment of mechanical properties and the stability of rock masses.This paper presents a novel approach to JRC evaluation using a genetic algorithm-optimized backpropagation(GA-BP)neural network.Conventional JRC evaluations have typically depended on two-dimensional(2D)and three-dimensional(3D)parameter calculation methods,which fail to fully capture the nonlinear relationship between the complex surface morphology of joints and their roughness.Our analysis from shear tests on eight different joint types revealed that the strength and failure characteristics of the joints not only exhibit directional dependence but also positively correlate with surface dip angles,heights,and back slope morphological features.Subsequently,five simple statistical parameters,i.e.average dip angle,median dip angle,average height,height coefficient of variation,and back slope feature value(K),were utilized to quantify these characteristics.For the prediction of JRC,we compiled and analyzed 105 datasets,each containing these five statistical parameters and their corresponding JRC values.A GA-BP neural network model was then constructed using this dataset,with the five morphological characteristic statistics serving as inputs and the JRC values as outputs.A comparative analysis was performed between the GA-BP neural network model,the statistical parameter method,and the fractal parameter method.This analysis confirmed that our proposed method offers higher accuracy in evaluating the roughness coefficient and shear strength of joints.展开更多
In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of ind...In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.展开更多
To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is propos...To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is proposed.First,on the basis of the PSO-BPNN algorithm,the adaptive inertia weight is introduced to make the weight change with the fitness of the particle,the adaptive learning factor is used to obtain different search abilities in the early and later stages of the algorithm,the mutation operator is incorporated to increase the diversity of the population and avoid premature convergence,and the APSO-BPNN model is constructed.Then,the temperature of different measurement points of the motorized spindle is forecasted by the BPNN,PSO-BPNN,and APSO-BPNN models.The experimental results demonstrate that the APSO-BPNN model has a significant advantage over the other two methods regarding prediction precision and robustness.The presented algorithm can provide a theoretical basis for intelligently controlling temperature and developing an early warning system for high-speed motorized spindles and machine tools.展开更多
The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic ...The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic flow forecasting; however, BPNN is easy to fall into local optimum and slow convergence. In order to overcome these deficiencies, a new approach called social emotion optimization algorithm(SEOA) is proposed in this paper to optimize the linked weights and thresholds of BPNN. Each individual in SEOA represents a BPNN. The availability of the proposed forecasting models is proved with the actual traffic flow data of the 2 nd Ring Road of Beijing. Experiment of results show that the forecasting accuracy of SEOA is improved obviously as compared with the accuracy of particle swarm optimization back-propagation(PSOBP) and simulated annealing particle swarm optimization back-propagation(SAPSOBP) models. Furthermore, since SEOA does not respond to the negative feedback information, Metropolis rule is proposed to give consideration to both positive and negative feedback information and diversify the adjustment methods. The modified BPNN model, in comparison with social emotion optimization back-propagation(SEOBP) model, is more advantageous to search the global optimal solution. The accuracy of Metropolis rule social emotion optimization back-propagation(MRSEOBP) model is improved about 19.54% as compared with that of SEOBP model in predicting the dramatically changing data.展开更多
Rainstorms are one of the most important types of natural disaster in China.In order to enhance the ability to forecast rainstorms in the short term,this paper explores how to combine a back-propagation neural network...Rainstorms are one of the most important types of natural disaster in China.In order to enhance the ability to forecast rainstorms in the short term,this paper explores how to combine a back-propagation neural network(BPNN)with synoptic diagnosis for predicting rainstorms,and analyzes the hit rates of rainstorms for the above two methods using the county of Tianquan as a case study.Results showed that the traditional synoptic diagnosis method still has an important referential meaning for most rainstorm types through synoptic typing and statistics of physical quantities based on historical cases,and the threat score(TS)of rainstorms was more than 0.75.However,the accuracy for two rainstorm types influenced by low-level easterly inverted troughs was less than 40%.The BPNN method efficiently forecasted these two rainstorm types;the TS and equitable threat score(ETS)of rainstorms were 0.80 and 0.79,respectively.The TS and ETS of the hybrid model that combined the BPNN and synoptic diagnosis methods exceeded the forecast score of multi-numerical simulations over the Sichuan Basin without exception.This kind of hybrid model enhanced the forecasting accuracy of rainstorms.The findings of this study provide certain reference value for the future development of refined forecast models with local features.展开更多
A nonparametric structural damage detection methodology based on neuralnetworks method is presented for health monitoring of structure-unknown systems. In this approachappropriate neural networks are trained by use of...A nonparametric structural damage detection methodology based on neuralnetworks method is presented for health monitoring of structure-unknown systems. In this approachappropriate neural networks are trained by use of the modal test data from a 'healthy' structure.The trained networks which are subsequently fed with vibration measurements from the same structurein different stages have the capability of recognizing the location and the content of structuraldamage and thereby can monitor the health of the structure. A modified back-propagation neuralnetwork is proposed to solve the two practical problems encountered by the traditionalback-propagation method, i.e., slow learning progress and convergence to a false local minimum.Various training algorithms, types of the input layer and numbers of the nodes in the input layerare considered. Numerical example results from a 5-degree-of-freedom spring-mass structure andanalyses on the experimental data of an actual 5-storey-steel-frame demonstrate thatneural-networks-based method is a robust procedure and a practical tool for the detection ofstructural damage, and that the modified back-propagation algorithm could improve the computationalefficiency as well as the accuracy of detection.展开更多
To reduce typhoon-caused damages, numerical and empirical methods are often used to forecast typhoon storm surge. However, typhoon surge is a complex nonlinear process that is difficult to forecast accurately. We appl...To reduce typhoon-caused damages, numerical and empirical methods are often used to forecast typhoon storm surge. However, typhoon surge is a complex nonlinear process that is difficult to forecast accurately. We applied a principal component back-propagation neural network (PCBPNN) to predict the deviation in typhoon storm surge, in which data of the typhoon, upstream flood, and historical case studies were involved. With principal component analysis, 15 input factors were reduced to five principal components, and the application of the model was improved. Observation data from Huangpu Park in Shanghai, China were used to test the feasibility of the model. The results indicate that the model is capable of predicting a 12-hour warning before a typhoon surge.展开更多
In this work, datasets of water and carbon fluxes measured with eddy covariance technique above a summer maize field in the North China Plain were simulated with artificial neural networks (ANNs) to explore the fluxes...In this work, datasets of water and carbon fluxes measured with eddy covariance technique above a summer maize field in the North China Plain were simulated with artificial neural networks (ANNs) to explore the fluxes responses to local environmental variables. The results showed that photosynthetically active radiation (PAR), vapor pressure deficit (VPD), air temperature (T) and leaf area index (LAI) were primary factors regulating both water vapor and carbon dioxide fluxes. Three-layer back-propagation neural networks (BP) could be applied to model fluxes exchange between cropland surface and atmosphere without using detailed physiological information or specific parameters of the plant.展开更多
Materials data deep-excavation is very important in materials genome exploration.In order to carry out materials data deep-excavation in hot die steels and obtain the relationships among alloying elements,heat treatme...Materials data deep-excavation is very important in materials genome exploration.In order to carry out materials data deep-excavation in hot die steels and obtain the relationships among alloying elements,heat treatment parameters and materials properties,a 11×12×12×4 back-propagation（BP）artificial neural network（ANN）was set up.Alloying element contents,quenching and tempering temperatures were selected as input;hardness,tensile and yield strength were set as output parameters.The ANN shows a high fitting precision.The effects of alloying elements and heat treatment parameters on the properties of hot die steel were studied using this model.The results indicate that high temperature hardness increases with increasing alloying element content of C,Si,Mo,W,Ni,V and Cr to a maximum value and decreases with further increase in alloying element content.The ANN also predicts that the high temperature hardness will decrease with increasing quenching temperature,and possess an optimal value with increasing tempering temperature.This model provides a new tool for novel hot die steel design.展开更多
Xigeda formation is a type of hundredmeter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located...Xigeda formation is a type of hundredmeter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located near Yonglang Town of Dechang County in Sichuan Province of China, which was a typical Xigeda formation landslide, was stabilized by anti-slide piles. Loading tests on a loading-test pile were conducted to measure the displacements and moments. The uncertainty of the tested geomechanical parameters of the Yonglang landslide over certain ranges would be problematic during the evaluation of the landslide. Thus, uniform design was introduced in the experimental design,and by which, numerical analyses of the loading-test pile were performed using Fast Lagrangian Analysis of Continua(FLAC3D) to acquire a database of the geomechanical parameters of the Yonglang landslide and the corresponding displacements of the loadingtest pile. A three-layer back-propagation neural network was established and trained with the database, and then tested and verified for its accuracy and reliability in numerical simulations. Displacement back analysis was conducted by substituting the displacements of the loading-test pile to the well-trained three-layer back-propagation neural network so as to identify the geomechanical parameters of the Yonglang landslide. The neuralnetwork-based displacement back analysis method with the proposed methodology is verified to be accurate and reliable for the identification of the uncertain geomechanical parameters of landslides.展开更多
Phase pure ZrB2-SiC composite powders were prepared after 1 450℃/3 h via carbothermal reduction route,by using ZrSiO4,B2O3 and carbon as the raw materials.The influences of firing temperature as well as the type and ...Phase pure ZrB2-SiC composite powders were prepared after 1 450℃/3 h via carbothermal reduction route,by using ZrSiO4,B2O3 and carbon as the raw materials.The influences of firing temperature as well as the type and amount of additive on the phase composition of final products were detailedly investigated.The results indicated that the onset formation temperature of ZrB2-SiC was reduced to 1 400℃by the present conditions,and oxide additive（including CoSO4·7H2O,Y2O3 and TiO2）was effective in enhancing the decomposition of raw ZrSiO4,therefore accelerating the synthesis of ZrB2-SiC.Moreover,microstructural observation showed that the as-prepared ZrB2 and SiC respectively had well-defined hexagonal columnar and fibrous morphology.Furthermore,the methodology of back-propagation artificial neural networks（BP-ANNs）was adopted to establish a model for predicting the reaction extent（e g,the content of ZrB2-SiC in final product）in terms of various processing conditions.The results predicted by the as-established BP-ANNs model matched well with that of testing experiment（with a mean square error in 10^（-3） degree）,verifying good effectiveness of the proposed strategy.展开更多
This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of ve...This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of vehicle interior noises under operating conditions, including idle, constant speed, accelerating and braking, are acquired. The objective psychoacoustic parameters and subjective annoyance results are respectively used as the input and output of the BPNN-SQP model. With correlation analysis and significance test, some psychoacoustic parameters, such as loudness, A-weighted sound pressure level, roughness, articulation index and sharpness, are selected for modeling. The annoyance values of unknown noise samples estimated by the BPNN-SQP model are highly correlated with the subjective annoyances. Conclusion can be drawn that the proposed BPNN-SQP model has good generalization ability and can be applied in sound quality prediction of vehicle interior noise under multiple working conditions.展开更多
A back-propagation neural network （BPNN） was used to establish relationships between the shortrange （0-3-h） rainfall and the predictors ranging from extrapolative forecasts of radar reflectivity, satelliteestimate...A back-propagation neural network （BPNN） was used to establish relationships between the shortrange （0-3-h） rainfall and the predictors ranging from extrapolative forecasts of radar reflectivity, satelliteestimated cloud-top temperature, lightning strike rates, and Nested Grid Model （NGM） outputs. Quan- titative precipitation forecasts （QPF） and the probabilities of categorical precipitation were obtained. Results of the BPNN algorithm were compared to the results obtained from the multiple linear regression algorithm for an independent dataset from the 1999 warm season over the continental United States. A sample forecast was made over the southeastern United States. Results showed that the BPNN categorical rainfall forecasts agreed well with Stage Ⅲ observations in terms of the size and shape of the area of rainfall. The BPNN tended to over-forecast the spatial extent of heavier rainfall amounts, but the positioning of the areas with rainfall ≥25.4 mm was still generally accurate. It appeared that the BPNN and linear regression approaches produce forecasts of very similar quality, although in some respects BPNN slightly outperformed the regression.展开更多
The solubility data of compounds in supercritical fluids and the correlation between the experimental solubility data and predicted solubility data are crucial to the development of supercritical technologies. In the ...The solubility data of compounds in supercritical fluids and the correlation between the experimental solubility data and predicted solubility data are crucial to the development of supercritical technologies. In the present work, the solubility data of silymarin(SM) in both pure supercritical carbon dioxide(SCCO2) and SCCO2 with added cosolvent was measured at temperatures ranging from 308 to 338 K and pressures from 8 to 22 MPa. The experimental data were fit with three semi-empirical density-based models(Chrastil, Bartle and Mendez-Santiago and Teja models) and a back-propagation artificial neural networks(BPANN) model. Interaction parameters for the models were obtained and the percentage of average absolute relative deviation(AARD%) in each calculation was determined. The correlation results were in good agreement with the experimental data. A comparison among the four models revealed that the experimental solubility data were more fit with the BPANN model with AARDs ranging from 1.14% to 2.15% for silymarin in pure SCCO2 and with added cosolvent. The results provide fundamental data for designing the extraction of SM or the preparation of its particle using SCCO2 techniques.展开更多
To overcome the obstacle of the fascinating relation in predicting animal phenotype value, we have developed a neural network model to detect the complex non-linear relationships between the genotypes and phenotypes a...To overcome the obstacle of the fascinating relation in predicting animal phenotype value, we have developed a neural network model to detect the complex non-linear relationships between the genotypes and phenotypes and the possible interactions that cannot be expressed with equations. In this paper, back-propagation neural network is used to discuss the influences of different allele frequencies on estimating the polygenic phenotype value. To ensure the precision of prediction, normalization was needed to train the prediction model. The results show that back-propagation artificial neural networks can be used to predict the phenotype value and perform very well in allele frequency from 0.2 to 0.8, when the allele frequency is very small （less than 0.2） or big （more than 0.8）; however, the prediction model was not reliable and the predicted value should be carefully tested.展开更多
Back-propagation neural network was applied to predict and optimize the synthetic technology of 2-chloro-4,6-dinitroresorcinol. A model was established based on back-propagation neural network using the experimental d...Back-propagation neural network was applied to predict and optimize the synthetic technology of 2-chloro-4,6-dinitroresorcinol. A model was established based on back-propagation neural network using the experimental data of homogeneous design as the training sample set and the technological parameters were optimized by it. The optimal technological parameters are as follows： the reaction time is 4h, the reaction temperature is 80℃, the molar ratio of NaOH to 4,6-dinitro-1,2,3-trichlorobenzene is 5.5：1, the molar ratio of methanol to 4,6-dinitro-1,2,3- trichlorobenzene is 11：1, and the molar ratio of water to 4,6-dinitro-1,2,3-trichlorobenzene is 70：1. Under the optimal conditions, three groups of experiments were performed and the average yield of 2-chloro-4,6-dinitroresorcinol is 96.64%, the absolute error of it with the predicted value is -1.07%.展开更多
A method for predicting the five species contents of cadmium was developed by combining the back-propagation artificial neural network with graphite furnace atomic absorption spectrometry（BP-ANN-GF-AAS）.Based on the...A method for predicting the five species contents of cadmium was developed by combining the back-propagation artificial neural network with graphite furnace atomic absorption spectrometry（BP-ANN-GF-AAS）.Based on the strong learning function and the features of the information distributed storage of artificial neural network（ANN）,a single ANN was constituted in which only one determination point of every sample was required.The exchangeable,carbonated,Fe-Mn oxidable,organic and residual species of cadmium for 20 kinds of soil samples from the two sections of Changchun（China） were determined by BP-ANN-GF-AAS.The detection limit of the method is 0.024 μg/L and the limit of quantification is 0.080 μg/L.t-Test indicates that there is not any systemic error of the results obtained by the Tessier sequential extraction graphite furnace atomic absorption spectrometry method（Tessier-GF-AAS） and BP-ANN-GF-AAS.Compared with those of the Tessier-GF-AAS,the prediction errors of BP-ANN-GF-AAS are less than 10%.The proposed method is fast,convenient,sensitive,and can eliminate the interference among various species.展开更多
ANNs （Artificial neural networks） are used extensively in remote sensing image processing. It has been proven that BPNNs （back-propagation neural networks） have high attainable classification accuracy. However, th...ANNs （Artificial neural networks） are used extensively in remote sensing image processing. It has been proven that BPNNs （back-propagation neural networks） have high attainable classification accuracy. However, there is a noticeable variation in the achieved accuracies due to different network designs and implementations. Hence, researchers usually need to conduct several experimental trials before they can finalize the network design. This is a time consuming process which significantly reduces the effectiveness of using BPNNs and the final design may still not be optimal. Therefore, there is a need to see whether there are some common guidelines for effective design and implementation of BPNNs. With this aim in mind, this paper attempts to find and summarize the common guidelines suggested by different authors through literature review and discussion of the findings. To provide readers with background and contextual information, some ANN fundamentals are also introduced.展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.41571217)the National Key Research and Development Program of China(No.2016YFD0300801)
文摘Accurate mapping of soil salinity and recognition of its influencing factors are essential for sustainable crop production and soil health. Although the influencing factors have been used to improve the mapping accuracy of soil salinity, few studies have considered both aspects of spatial variation caused by the influencing factors and spatial autocorrelations for mapping. The objective of this study was to demonstrate that the ordinary kriging combined with back-propagation network(OK_BP), considering the two aspects of spatial variation, which can benefit the improvement of the mapping accuracy of soil salinity. To test the effectiveness of this approach, 70 sites were sampled at two depths(0–30 and 30–50 cm) in Ningxia Hui Autonomous Region, China. Ordinary kriging(OK), back-propagation network(BP) and regression kriging(RK) were used in comparison analysis; the root mean square error(RMSE), relative improvement(RI) and the decrease in estimation imprecision(DIP) were used to judge the mapping quality. Results showed that OK_BP avoided the both underestimation and overestimation of the higher and lower values of interpolation surfaces. OK_BP revealed more details of the spatial variation responding to influencing factors, and provided more flexibility for incorporating various correlated factors in the mapping. Moreover, OK_BP obtained better results with respect to the reference methods(i.e., OK, BP, and RK) in terms of the lowest RMSE, the highest RI and DIP. Thus, it is concluded that OK_BP is an effective method for mapping soil salinity with a high accuracy.
基金funded by the National Natural Science Foundation of China(Grant Nos.42472345,52325905,42407202).
文摘The joint roughness coefficient(JRC)is a key parameter in the assessment of mechanical properties and the stability of rock masses.This paper presents a novel approach to JRC evaluation using a genetic algorithm-optimized backpropagation(GA-BP)neural network.Conventional JRC evaluations have typically depended on two-dimensional(2D)and three-dimensional(3D)parameter calculation methods,which fail to fully capture the nonlinear relationship between the complex surface morphology of joints and their roughness.Our analysis from shear tests on eight different joint types revealed that the strength and failure characteristics of the joints not only exhibit directional dependence but also positively correlate with surface dip angles,heights,and back slope morphological features.Subsequently,five simple statistical parameters,i.e.average dip angle,median dip angle,average height,height coefficient of variation,and back slope feature value(K),were utilized to quantify these characteristics.For the prediction of JRC,we compiled and analyzed 105 datasets,each containing these five statistical parameters and their corresponding JRC values.A GA-BP neural network model was then constructed using this dataset,with the five morphological characteristic statistics serving as inputs and the JRC values as outputs.A comparative analysis was performed between the GA-BP neural network model,the statistical parameter method,and the fractal parameter method.This analysis confirmed that our proposed method offers higher accuracy in evaluating the roughness coefficient and shear strength of joints.
基金Project(50734007) supported by the National Natural Science Foundation of China
文摘In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.
基金The National Natural Science Foundation of China(No.51465035)the Natural Science Foundation of Gansu,China(No.20JR5R-A466)。
文摘To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is proposed.First,on the basis of the PSO-BPNN algorithm,the adaptive inertia weight is introduced to make the weight change with the fitness of the particle,the adaptive learning factor is used to obtain different search abilities in the early and later stages of the algorithm,the mutation operator is incorporated to increase the diversity of the population and avoid premature convergence,and the APSO-BPNN model is constructed.Then,the temperature of different measurement points of the motorized spindle is forecasted by the BPNN,PSO-BPNN,and APSO-BPNN models.The experimental results demonstrate that the APSO-BPNN model has a significant advantage over the other two methods regarding prediction precision and robustness.The presented algorithm can provide a theoretical basis for intelligently controlling temperature and developing an early warning system for high-speed motorized spindles and machine tools.
基金the Research of New Intelligent Integrated Transport Information System,Technical Plan Project of Binhai New District,Tianjin(No.2015XJR21017)
文摘The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic flow forecasting; however, BPNN is easy to fall into local optimum and slow convergence. In order to overcome these deficiencies, a new approach called social emotion optimization algorithm(SEOA) is proposed in this paper to optimize the linked weights and thresholds of BPNN. Each individual in SEOA represents a BPNN. The availability of the proposed forecasting models is proved with the actual traffic flow data of the 2 nd Ring Road of Beijing. Experiment of results show that the forecasting accuracy of SEOA is improved obviously as compared with the accuracy of particle swarm optimization back-propagation(PSOBP) and simulated annealing particle swarm optimization back-propagation(SAPSOBP) models. Furthermore, since SEOA does not respond to the negative feedback information, Metropolis rule is proposed to give consideration to both positive and negative feedback information and diversify the adjustment methods. The modified BPNN model, in comparison with social emotion optimization back-propagation(SEOBP) model, is more advantageous to search the global optimal solution. The accuracy of Metropolis rule social emotion optimization back-propagation(MRSEOBP) model is improved about 19.54% as compared with that of SEOBP model in predicting the dramatically changing data.
基金supported by the National Key Research and Development Program on Monitoring,Early Warning and Prevention of Major Natural Disasters [grant number 2018YFC1506006]the National Natural Science Foundation of China [grant numbers 41805054 and U20A2097]。
文摘Rainstorms are one of the most important types of natural disaster in China.In order to enhance the ability to forecast rainstorms in the short term,this paper explores how to combine a back-propagation neural network(BPNN)with synoptic diagnosis for predicting rainstorms,and analyzes the hit rates of rainstorms for the above two methods using the county of Tianquan as a case study.Results showed that the traditional synoptic diagnosis method still has an important referential meaning for most rainstorm types through synoptic typing and statistics of physical quantities based on historical cases,and the threat score(TS)of rainstorms was more than 0.75.However,the accuracy for two rainstorm types influenced by low-level easterly inverted troughs was less than 40%.The BPNN method efficiently forecasted these two rainstorm types;the TS and equitable threat score(ETS)of rainstorms were 0.80 and 0.79,respectively.The TS and ETS of the hybrid model that combined the BPNN and synoptic diagnosis methods exceeded the forecast score of multi-numerical simulations over the Sichuan Basin without exception.This kind of hybrid model enhanced the forecasting accuracy of rainstorms.The findings of this study provide certain reference value for the future development of refined forecast models with local features.
基金the National Natural Science Foundation of China (No.59908003)the Natural Science Foundation of Hubei Province (No.99J035)
文摘A nonparametric structural damage detection methodology based on neuralnetworks method is presented for health monitoring of structure-unknown systems. In this approachappropriate neural networks are trained by use of the modal test data from a 'healthy' structure.The trained networks which are subsequently fed with vibration measurements from the same structurein different stages have the capability of recognizing the location and the content of structuraldamage and thereby can monitor the health of the structure. A modified back-propagation neuralnetwork is proposed to solve the two practical problems encountered by the traditionalback-propagation method, i.e., slow learning progress and convergence to a false local minimum.Various training algorithms, types of the input layer and numbers of the nodes in the input layerare considered. Numerical example results from a 5-degree-of-freedom spring-mass structure andanalyses on the experimental data of an actual 5-storey-steel-frame demonstrate thatneural-networks-based method is a robust procedure and a practical tool for the detection ofstructural damage, and that the modified back-propagation algorithm could improve the computationalefficiency as well as the accuracy of detection.
基金Supported by National Marine Public Scientific Research Fund of China(No. 200905010)the Talent Training Fund Project for Basic Sciences of the National Natural Science Foundation of China (No. J0730534)+2 种基金the Fundamental Research Funds for the Central Universitiesthe Open Research Funding Program of KLGIS (No. KLGIS2011A12)the Open Fund from Key Laboratory of Marine Management Technique of State Oceanic Administration (No. 201112)
文摘To reduce typhoon-caused damages, numerical and empirical methods are often used to forecast typhoon storm surge. However, typhoon surge is a complex nonlinear process that is difficult to forecast accurately. We applied a principal component back-propagation neural network (PCBPNN) to predict the deviation in typhoon storm surge, in which data of the typhoon, upstream flood, and historical case studies were involved. With principal component analysis, 15 input factors were reduced to five principal components, and the application of the model was improved. Observation data from Huangpu Park in Shanghai, China were used to test the feasibility of the model. The results indicate that the model is capable of predicting a 12-hour warning before a typhoon surge.
基金Project (No. 40328001) supported by the National Science Fund forOutstanding Youth Overseas China
文摘In this work, datasets of water and carbon fluxes measured with eddy covariance technique above a summer maize field in the North China Plain were simulated with artificial neural networks (ANNs) to explore the fluxes responses to local environmental variables. The results showed that photosynthetically active radiation (PAR), vapor pressure deficit (VPD), air temperature (T) and leaf area index (LAI) were primary factors regulating both water vapor and carbon dioxide fluxes. Three-layer back-propagation neural networks (BP) could be applied to model fluxes exchange between cropland surface and atmosphere without using detailed physiological information or specific parameters of the plant.
文摘Materials data deep-excavation is very important in materials genome exploration.In order to carry out materials data deep-excavation in hot die steels and obtain the relationships among alloying elements,heat treatment parameters and materials properties,a 11×12×12×4 back-propagation（BP）artificial neural network（ANN）was set up.Alloying element contents,quenching and tempering temperatures were selected as input;hardness,tensile and yield strength were set as output parameters.The ANN shows a high fitting precision.The effects of alloying elements and heat treatment parameters on the properties of hot die steel were studied using this model.The results indicate that high temperature hardness increases with increasing alloying element content of C,Si,Mo,W,Ni,V and Cr to a maximum value and decreases with further increase in alloying element content.The ANN also predicts that the high temperature hardness will decrease with increasing quenching temperature,and possess an optimal value with increasing tempering temperature.This model provides a new tool for novel hot die steel design.
基金supported by the "Light of West China" Program of Chinese Academy of Sciences (Grant No.Y6R2250250)the National Basic Research Program of China (973 Program, Grant No.2013CB733201)+2 种基金the One-Hundred Talents Program of Chinese Academy of Sciences (LijunSu)the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (Grant No.QYZDB-SSW-DQC010)the Youth Fund of Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (Grant No. Y6K2110110)
文摘Xigeda formation is a type of hundredmeter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located near Yonglang Town of Dechang County in Sichuan Province of China, which was a typical Xigeda formation landslide, was stabilized by anti-slide piles. Loading tests on a loading-test pile were conducted to measure the displacements and moments. The uncertainty of the tested geomechanical parameters of the Yonglang landslide over certain ranges would be problematic during the evaluation of the landslide. Thus, uniform design was introduced in the experimental design,and by which, numerical analyses of the loading-test pile were performed using Fast Lagrangian Analysis of Continua(FLAC3D) to acquire a database of the geomechanical parameters of the Yonglang landslide and the corresponding displacements of the loadingtest pile. A three-layer back-propagation neural network was established and trained with the database, and then tested and verified for its accuracy and reliability in numerical simulations. Displacement back analysis was conducted by substituting the displacements of the loading-test pile to the well-trained three-layer back-propagation neural network so as to identify the geomechanical parameters of the Yonglang landslide. The neuralnetwork-based displacement back analysis method with the proposed methodology is verified to be accurate and reliable for the identification of the uncertain geomechanical parameters of landslides.
基金Funded by National Natural Science Foundation of China(Nos.51502212,51672194 and 51472184)Hubei Province Natural Science Foundation of China(No.2018CFB760)+1 种基金Program for Innovative Teams of Outstanding Young and Middle-aged Researchers in the Higher Education Institutions of Hubei Province(No.T201602)Key Program of Natural Science Foundation of Hubei Province(No.2017CFA004)
文摘Phase pure ZrB2-SiC composite powders were prepared after 1 450℃/3 h via carbothermal reduction route,by using ZrSiO4,B2O3 and carbon as the raw materials.The influences of firing temperature as well as the type and amount of additive on the phase composition of final products were detailedly investigated.The results indicated that the onset formation temperature of ZrB2-SiC was reduced to 1 400℃by the present conditions,and oxide additive（including CoSO4·7H2O,Y2O3 and TiO2）was effective in enhancing the decomposition of raw ZrSiO4,therefore accelerating the synthesis of ZrB2-SiC.Moreover,microstructural observation showed that the as-prepared ZrB2 and SiC respectively had well-defined hexagonal columnar and fibrous morphology.Furthermore,the methodology of back-propagation artificial neural networks（BP-ANNs）was adopted to establish a model for predicting the reaction extent（e g,the content of ZrB2-SiC in final product）in terms of various processing conditions.The results predicted by the as-established BP-ANNs model matched well with that of testing experiment（with a mean square error in 10^（-3） degree）,verifying good effectiveness of the proposed strategy.
文摘This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of vehicle interior noises under operating conditions, including idle, constant speed, accelerating and braking, are acquired. The objective psychoacoustic parameters and subjective annoyance results are respectively used as the input and output of the BPNN-SQP model. With correlation analysis and significance test, some psychoacoustic parameters, such as loudness, A-weighted sound pressure level, roughness, articulation index and sharpness, are selected for modeling. The annoyance values of unknown noise samples estimated by the BPNN-SQP model are highly correlated with the subjective annoyances. Conclusion can be drawn that the proposed BPNN-SQP model has good generalization ability and can be applied in sound quality prediction of vehicle interior noise under multiple working conditions.
文摘A back-propagation neural network （BPNN） was used to establish relationships between the shortrange （0-3-h） rainfall and the predictors ranging from extrapolative forecasts of radar reflectivity, satelliteestimated cloud-top temperature, lightning strike rates, and Nested Grid Model （NGM） outputs. Quan- titative precipitation forecasts （QPF） and the probabilities of categorical precipitation were obtained. Results of the BPNN algorithm were compared to the results obtained from the multiple linear regression algorithm for an independent dataset from the 1999 warm season over the continental United States. A sample forecast was made over the southeastern United States. Results showed that the BPNN categorical rainfall forecasts agreed well with Stage Ⅲ observations in terms of the size and shape of the area of rainfall. The BPNN tended to over-forecast the spatial extent of heavier rainfall amounts, but the positioning of the areas with rainfall ≥25.4 mm was still generally accurate. It appeared that the BPNN and linear regression approaches produce forecasts of very similar quality, although in some respects BPNN slightly outperformed the regression.
基金supported financially by the Subject Chief Scientist Program (10XD14303900) from Science and Technology Commission of Shanghai Municipalitythe Special Research Fund for the Doctoral Program of Higher Education of China (20123107110005)
文摘The solubility data of compounds in supercritical fluids and the correlation between the experimental solubility data and predicted solubility data are crucial to the development of supercritical technologies. In the present work, the solubility data of silymarin(SM) in both pure supercritical carbon dioxide(SCCO2) and SCCO2 with added cosolvent was measured at temperatures ranging from 308 to 338 K and pressures from 8 to 22 MPa. The experimental data were fit with three semi-empirical density-based models(Chrastil, Bartle and Mendez-Santiago and Teja models) and a back-propagation artificial neural networks(BPANN) model. Interaction parameters for the models were obtained and the percentage of average absolute relative deviation(AARD%) in each calculation was determined. The correlation results were in good agreement with the experimental data. A comparison among the four models revealed that the experimental solubility data were more fit with the BPANN model with AARDs ranging from 1.14% to 2.15% for silymarin in pure SCCO2 and with added cosolvent. The results provide fundamental data for designing the extraction of SM or the preparation of its particle using SCCO2 techniques.
基金Supported by the Scientific Research Starting Foundation for Doctors, Henan Institute of Science and Technology of China
文摘To overcome the obstacle of the fascinating relation in predicting animal phenotype value, we have developed a neural network model to detect the complex non-linear relationships between the genotypes and phenotypes and the possible interactions that cannot be expressed with equations. In this paper, back-propagation neural network is used to discuss the influences of different allele frequencies on estimating the polygenic phenotype value. To ensure the precision of prediction, normalization was needed to train the prediction model. The results show that back-propagation artificial neural networks can be used to predict the phenotype value and perform very well in allele frequency from 0.2 to 0.8, when the allele frequency is very small （less than 0.2） or big （more than 0.8）; however, the prediction model was not reliable and the predicted value should be carefully tested.
文摘Back-propagation neural network was applied to predict and optimize the synthetic technology of 2-chloro-4,6-dinitroresorcinol. A model was established based on back-propagation neural network using the experimental data of homogeneous design as the training sample set and the technological parameters were optimized by it. The optimal technological parameters are as follows： the reaction time is 4h, the reaction temperature is 80℃, the molar ratio of NaOH to 4,6-dinitro-1,2,3-trichlorobenzene is 5.5：1, the molar ratio of methanol to 4,6-dinitro-1,2,3- trichlorobenzene is 11：1, and the molar ratio of water to 4,6-dinitro-1,2,3-trichlorobenzene is 70：1. Under the optimal conditions, three groups of experiments were performed and the average yield of 2-chloro-4,6-dinitroresorcinol is 96.64%, the absolute error of it with the predicted value is -1.07%.
基金Supported by the Fund of the State Key Laboratory of Electroanalytical Chemistry,Changchun Institute of Applied Chemistry,Chinese Academy of Sciences
文摘A method for predicting the five species contents of cadmium was developed by combining the back-propagation artificial neural network with graphite furnace atomic absorption spectrometry（BP-ANN-GF-AAS）.Based on the strong learning function and the features of the information distributed storage of artificial neural network（ANN）,a single ANN was constituted in which only one determination point of every sample was required.The exchangeable,carbonated,Fe-Mn oxidable,organic and residual species of cadmium for 20 kinds of soil samples from the two sections of Changchun（China） were determined by BP-ANN-GF-AAS.The detection limit of the method is 0.024 μg/L and the limit of quantification is 0.080 μg/L.t-Test indicates that there is not any systemic error of the results obtained by the Tessier sequential extraction graphite furnace atomic absorption spectrometry method（Tessier-GF-AAS） and BP-ANN-GF-AAS.Compared with those of the Tessier-GF-AAS,the prediction errors of BP-ANN-GF-AAS are less than 10%.The proposed method is fast,convenient,sensitive,and can eliminate the interference among various species.
文摘ANNs （Artificial neural networks） are used extensively in remote sensing image processing. It has been proven that BPNNs （back-propagation neural networks） have high attainable classification accuracy. However, there is a noticeable variation in the achieved accuracies due to different network designs and implementations. Hence, researchers usually need to conduct several experimental trials before they can finalize the network design. This is a time consuming process which significantly reduces the effectiveness of using BPNNs and the final design may still not be optimal. Therefore, there is a need to see whether there are some common guidelines for effective design and implementation of BPNNs. With this aim in mind, this paper attempts to find and summarize the common guidelines suggested by different authors through literature review and discussion of the findings. To provide readers with background and contextual information, some ANN fundamentals are also introduced.
