Support vector machine(SVM)is a new technology in data mining.It is a new tool to solve machine learning problems with the help of optimization.Support vector machines belong to a new machine learning that extends fro...Support vector machine(SVM)is a new technology in data mining.It is a new tool to solve machine learning problems with the help of optimization.Support vector machines belong to a new machine learning that extends from statistical learning theory.Its structure is relatively simple,with good generalization ability and global optimality.Support vector machine has provided a unified framework for solving finite sample learning problems,and there are many solutions proposed.It can deal with those more complex problems and introduce the characteristics of the support vector machine model.Aiming at the application of the model in economic forecasting,a method to improve the prediction accuracy of the model is proposed.The theoretical analysis and practical application verification are performed,which shows that this method can obtain more accurate prediction results.展开更多
Based on the statistical data from 1975 to 1997, we forecast the growth rate of coal consuming and the quantity in coming decade with the BP neuron network in the article.
How to obtain fast-growth errors, which is comparable to the actual forecast growth error, is a crucial problem in ensemble forecast （EF）. The method, Breeding of Growth Modes （BGM）, which has been used to generat...How to obtain fast-growth errors, which is comparable to the actual forecast growth error, is a crucial problem in ensemble forecast （EF）. The method, Breeding of Growth Modes （BGM）, which has been used to generate perturbations for medium-range EF at NCEP, simulates the development of fast-growth errors in the analysis cycle, and is a reasonable choice in capturing growing errors modes, especially for extreme weather by BGM. An ideal supercell storm, simulated by Weather Research Forecast model （WRF）, occurred in central Oklahoma on 20 May 1977. This simulation was used to study the application of BGM methods in the meso-scale strong convective Ensemble Prediction System （EPS）. We compared the forecasting skills of EPS by different pertubation methods, like Monte-Carlo and BGM. The results show that the ensemble average forecast based on Monte-Carlo with statistics meaning is superior to the single-deterministic prediction, but a less dynamic process of the method leads to a smaller spread than expected. The fast-growth errors of BGM are comparable to the actual short-range forecast error and a more appropriate ensemble spread. Considering evaluation indexes and scores, the forecast skills of EPS by BGM is higher than Monte-Carlo＇s. Furthermore, various breeding cycles have different effects on precipitation and non-precipitation fields, confirmation of reasonable cycles need consider balance between variables.展开更多
Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objective...Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.展开更多
For most commercial steels the prediction of the final properties depends on accurately calculating the room temperature ferrite grain size. A grain growth model is proposed for low carbon steels Q235B during hot roll...For most commercial steels the prediction of the final properties depends on accurately calculating the room temperature ferrite grain size. A grain growth model is proposed for low carbon steels Q235B during hot rolling. By using this model, the initial ferrite grain size after continuous cooling and ferrite grain growing in coiling procedure can be predicted. In-plant trials were performed in the hot strip mill of Ansteel. The calculated final ferrite grain sizes are in good agreement with the experimental ones. It is helpful both for simulation of microstructure evolution and prediction of mechanical properties.展开更多
基金supported by the Scientific Research program of Xinxiang University(Grant No.XXUTD20170108).
文摘Support vector machine(SVM)is a new technology in data mining.It is a new tool to solve machine learning problems with the help of optimization.Support vector machines belong to a new machine learning that extends from statistical learning theory.Its structure is relatively simple,with good generalization ability and global optimality.Support vector machine has provided a unified framework for solving finite sample learning problems,and there are many solutions proposed.It can deal with those more complex problems and introduce the characteristics of the support vector machine model.Aiming at the application of the model in economic forecasting,a method to improve the prediction accuracy of the model is proposed.The theoretical analysis and practical application verification are performed,which shows that this method can obtain more accurate prediction results.
文摘Based on the statistical data from 1975 to 1997, we forecast the growth rate of coal consuming and the quantity in coming decade with the BP neuron network in the article.
基金supported jointly by the Nature Science Foundation of China (Project No:40875068)Public-Welfare Meteorological Research Foundation (ProjectNo:GYHY200806029)
文摘How to obtain fast-growth errors, which is comparable to the actual forecast growth error, is a crucial problem in ensemble forecast （EF）. The method, Breeding of Growth Modes （BGM）, which has been used to generate perturbations for medium-range EF at NCEP, simulates the development of fast-growth errors in the analysis cycle, and is a reasonable choice in capturing growing errors modes, especially for extreme weather by BGM. An ideal supercell storm, simulated by Weather Research Forecast model （WRF）, occurred in central Oklahoma on 20 May 1977. This simulation was used to study the application of BGM methods in the meso-scale strong convective Ensemble Prediction System （EPS）. We compared the forecasting skills of EPS by different pertubation methods, like Monte-Carlo and BGM. The results show that the ensemble average forecast based on Monte-Carlo with statistics meaning is superior to the single-deterministic prediction, but a less dynamic process of the method leads to a smaller spread than expected. The fast-growth errors of BGM are comparable to the actual short-range forecast error and a more appropriate ensemble spread. Considering evaluation indexes and scores, the forecast skills of EPS by BGM is higher than Monte-Carlo＇s. Furthermore, various breeding cycles have different effects on precipitation and non-precipitation fields, confirmation of reasonable cycles need consider balance between variables.
基金supported by the National Natural Science Foundation of China(41561088 and 61501314)the Science&Technology Nova Program of Xinjiang Production and Construction Corps,China(2018CB020)
文摘Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.
基金financially supported by the National Key Basic Research and Development Programme of China (Grant No. G1998061512).
文摘For most commercial steels the prediction of the final properties depends on accurately calculating the room temperature ferrite grain size. A grain growth model is proposed for low carbon steels Q235B during hot rolling. By using this model, the initial ferrite grain size after continuous cooling and ferrite grain growing in coiling procedure can be predicted. In-plant trials were performed in the hot strip mill of Ansteel. The calculated final ferrite grain sizes are in good agreement with the experimental ones. It is helpful both for simulation of microstructure evolution and prediction of mechanical properties.
