在2010年植被碳密度空间分布结果的基础上,通过13个环境因子的1377个样点数据,建立径向基函数网络(Radial Basis Function Network,RBFN)模型,对桂西北喀斯特区植被碳密度空间分布的影响因素进行了初步探讨。研究结果显示:对该区植被碳...在2010年植被碳密度空间分布结果的基础上,通过13个环境因子的1377个样点数据,建立径向基函数网络(Radial Basis Function Network,RBFN)模型,对桂西北喀斯特区植被碳密度空间分布的影响因素进行了初步探讨。研究结果显示:对该区植被碳密度空间分布影响最为重要的前4位为地类、森林类型、林种和植被类型4个因子,其标准化重要性分别在50%以上;其次为石漠化程度、腐殖层厚度、面积等级、植被总覆盖度和土层厚度5个因子,其标准化的重要性分别在15%—30%;影响最小的是坡位、坡度、坡向和海拔4个地形因子,其标准化重要性仅2%—11%。研究表明地形因子对植被碳密度空间分布影响有限,更为重要的是土地类型、森林类型、林种和植被类型等可通过人为活动改变的因素,因此生态环境移民、退耕还林等石漠化治理措施对植被碳密度空间分布具有重要影响。展开更多
Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ri...Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ring's position is asymmetrical.All of these cause that the ring's dimensions cannot be measured directly.Through analyzing the relationships among the dimensions of ring blanks,the positions of rolls and the ring's inner and outer diameter,the soft measurement model of ring's dimensions is established based on the radial basis function neural network(RBFNN).A mass of data samples are obtained from VHRR finite element(FE) simulations to train and test the soft measurement NN model,and the model's structure parameters are deduced and optimized by genetic algorithm(GA).Finally,the soft measurement system of ring's dimensions is established and validated by the VHRR experiments.The ring's dimensions were measured artificially and calculated by the soft measurement NN model.The results show that the calculation values of GA-RBFNN model are close to the artificial measurement data.In addition,the calculation accuracy of GA-RBFNN model is higher than that of RBFNN model.The research results suggest that the soft measurement NN model has high precision and flexibility.The research can provide practical methods and theoretical guidance for the accurate measurement of VHRR process.展开更多
文摘在2010年植被碳密度空间分布结果的基础上,通过13个环境因子的1377个样点数据,建立径向基函数网络(Radial Basis Function Network,RBFN)模型,对桂西北喀斯特区植被碳密度空间分布的影响因素进行了初步探讨。研究结果显示:对该区植被碳密度空间分布影响最为重要的前4位为地类、森林类型、林种和植被类型4个因子,其标准化重要性分别在50%以上;其次为石漠化程度、腐殖层厚度、面积等级、植被总覆盖度和土层厚度5个因子,其标准化的重要性分别在15%—30%;影响最小的是坡位、坡度、坡向和海拔4个地形因子,其标准化重要性仅2%—11%。研究表明地形因子对植被碳密度空间分布影响有限,更为重要的是土地类型、森林类型、林种和植被类型等可通过人为活动改变的因素,因此生态环境移民、退耕还林等石漠化治理措施对植被碳密度空间分布具有重要影响。
基金Project(51205299)supported by the National Natural Science Foundation of ChinaProject(2015M582643)supported by the China Postdoctoral Science Foundation+2 种基金Project(2014BAA008)supported by the Science and Technology Support Program of Hubei Province,ChinaProject(2014-IV-144)supported by the Fundamental Research Funds for the Central Universities of ChinaProject(2012AAA07-01)supported by the Major Science and Technology Achievements Transformation&Industrialization Program of Hubei Province,China
文摘Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ring's position is asymmetrical.All of these cause that the ring's dimensions cannot be measured directly.Through analyzing the relationships among the dimensions of ring blanks,the positions of rolls and the ring's inner and outer diameter,the soft measurement model of ring's dimensions is established based on the radial basis function neural network(RBFNN).A mass of data samples are obtained from VHRR finite element(FE) simulations to train and test the soft measurement NN model,and the model's structure parameters are deduced and optimized by genetic algorithm(GA).Finally,the soft measurement system of ring's dimensions is established and validated by the VHRR experiments.The ring's dimensions were measured artificially and calculated by the soft measurement NN model.The results show that the calculation values of GA-RBFNN model are close to the artificial measurement data.In addition,the calculation accuracy of GA-RBFNN model is higher than that of RBFNN model.The research results suggest that the soft measurement NN model has high precision and flexibility.The research can provide practical methods and theoretical guidance for the accurate measurement of VHRR process.
