Real coded Accelerating Genetic Algorithm （RAGA）, Chaos Algorithm （CA） were used to solve the sensitivity index of Jensen model which is one of models of crop water production function. After comparing with the ou...Real coded Accelerating Genetic Algorithm （RAGA）, Chaos Algorithm （CA） were used to solve the sensitivity index of Jensen model which is one of models of crop water production function. After comparing with the outcome of Least Square Regression （LSR）, the result showed that RAGA not only had high accuracy and more effective, but also saved calculating time. The authors provides new effective methods for calculating index of crop water production function.展开更多
In ultra-high-dimensional data, it is common for the response variable to be multi-classified. Therefore, this paper proposes a model-free screening method for variables whose response variable is multi-classified fro...In ultra-high-dimensional data, it is common for the response variable to be multi-classified. Therefore, this paper proposes a model-free screening method for variables whose response variable is multi-classified from the point of view of introducing Jensen-Shannon divergence to measure the importance of covariates. The idea of the method is to calculate the Jensen-Shannon divergence between the conditional probability distribution of the covariates on a given response variable and the unconditional probability distribution of the covariates, and then use the probabilities of the response variables as weights to calculate the weighted Jensen-Shannon divergence, where a larger weighted Jensen-Shannon divergence means that the covariates are more important. Additionally, we also investigated an adapted version of the method, which is to measure the relationship between the covariates and the response variable using the weighted Jensen-Shannon divergence adjusted by the logarithmic factor of the number of categories when the number of categories in each covariate varies. Then, through both theoretical and simulation experiments, it was demonstrated that the proposed methods have sure screening and ranking consistency properties. Finally, the results from simulation and real-dataset experiments show that in feature screening, the proposed methods investigated are robust in performance and faster in computational speed compared with an existing method.展开更多
On basis of test information, the research performed analysis on water production function models of two crops, which indicated that water model of crops in whole growth stage and water model of crops indifferent grow...On basis of test information, the research performed analysis on water production function models of two crops, which indicated that water model of crops in whole growth stage and water model of crops indifferent growth stages have consistency as well as differences, providing references for optimization of irrigation water. Meanwhile, the research analyzed the deficiency of optimization on irrigation water for crops just by Jensen model.展开更多
The Loess Plateau has a typical semi-arid climate, and the area suffers from very harsh ecological environment, severe soil erosion and water runoff, and uneven distributed precipitation. Due to the relatively low hol...The Loess Plateau has a typical semi-arid climate, and the area suffers from very harsh ecological environment, severe soil erosion and water runoff, and uneven distributed precipitation. Due to the relatively low holding capacity, current rainwater-collecting and conservation facilities can only supplement a maximum of18 mm of water for crop production in each irrigation. In this study, mathematical models were constructed to identify the water requirement critical period of maize crop by evaluating response of each individual developmental stage to supplemental irrigation with harvested rainwater. In the transformed Jensen model, ETmin/Eta was used as the index of relative evapotranspiration. The use of relative yield and relative crop evapotranspiration was able to eliminate influences from unintended environmental factors. A BP neural network crop-water model for extreme water deficit condition was constructed using the index of relative evapotranspiration as the input and the index of relative yield as the output after iterative training and adjustment of weight values. Comparison of measured maize yields to those predicted by the two models confirmed that the BP neural network crop-water model is more accurate than the transformed Jensen model in predicting the sensitivity index to waterdeficit at various growth stages and maize yield when provided with supplemental irrigation with harvested rainwater.展开更多
基金Supported by Science and Technology Research Program of Heilongjiang Province(GB06B106-7)
文摘Real coded Accelerating Genetic Algorithm （RAGA）, Chaos Algorithm （CA） were used to solve the sensitivity index of Jensen model which is one of models of crop water production function. After comparing with the outcome of Least Square Regression （LSR）, the result showed that RAGA not only had high accuracy and more effective, but also saved calculating time. The authors provides new effective methods for calculating index of crop water production function.
文摘In ultra-high-dimensional data, it is common for the response variable to be multi-classified. Therefore, this paper proposes a model-free screening method for variables whose response variable is multi-classified from the point of view of introducing Jensen-Shannon divergence to measure the importance of covariates. The idea of the method is to calculate the Jensen-Shannon divergence between the conditional probability distribution of the covariates on a given response variable and the unconditional probability distribution of the covariates, and then use the probabilities of the response variables as weights to calculate the weighted Jensen-Shannon divergence, where a larger weighted Jensen-Shannon divergence means that the covariates are more important. Additionally, we also investigated an adapted version of the method, which is to measure the relationship between the covariates and the response variable using the weighted Jensen-Shannon divergence adjusted by the logarithmic factor of the number of categories when the number of categories in each covariate varies. Then, through both theoretical and simulation experiments, it was demonstrated that the proposed methods have sure screening and ranking consistency properties. Finally, the results from simulation and real-dataset experiments show that in feature screening, the proposed methods investigated are robust in performance and faster in computational speed compared with an existing method.
文摘On basis of test information, the research performed analysis on water production function models of two crops, which indicated that water model of crops in whole growth stage and water model of crops indifferent growth stages have consistency as well as differences, providing references for optimization of irrigation water. Meanwhile, the research analyzed the deficiency of optimization on irrigation water for crops just by Jensen model.
基金Supported by Inner Mongolia water conservancy"Twelfth five-year"Major Science and Technology Demonstration Project-scientific Support Project for New Water-saving Irrigation Area of Four ten Million mu in Inner Mongolia in China(20121036)the National Natural Science Foundation of China(No.51469026,2012MS0621)the Guided Reward Fund for Scientific and Technological Innovation,Inner Mongolia,China
文摘The Loess Plateau has a typical semi-arid climate, and the area suffers from very harsh ecological environment, severe soil erosion and water runoff, and uneven distributed precipitation. Due to the relatively low holding capacity, current rainwater-collecting and conservation facilities can only supplement a maximum of18 mm of water for crop production in each irrigation. In this study, mathematical models were constructed to identify the water requirement critical period of maize crop by evaluating response of each individual developmental stage to supplemental irrigation with harvested rainwater. In the transformed Jensen model, ETmin/Eta was used as the index of relative evapotranspiration. The use of relative yield and relative crop evapotranspiration was able to eliminate influences from unintended environmental factors. A BP neural network crop-water model for extreme water deficit condition was constructed using the index of relative evapotranspiration as the input and the index of relative yield as the output after iterative training and adjustment of weight values. Comparison of measured maize yields to those predicted by the two models confirmed that the BP neural network crop-water model is more accurate than the transformed Jensen model in predicting the sensitivity index to waterdeficit at various growth stages and maize yield when provided with supplemental irrigation with harvested rainwater.
