参考作物腾发量(ET0)是计算作物需水量、制定灌溉制度和进行水资源管理的主要参数之一。计算参考作物腾发量(ET0)的方法众多,为规范ET0的求法,联合国粮农组织(FAO)推荐采用修改的Penm an-M on te ith方法。该文指出不需要收集长序列气...参考作物腾发量(ET0)是计算作物需水量、制定灌溉制度和进行水资源管理的主要参数之一。计算参考作物腾发量(ET0)的方法众多,为规范ET0的求法,联合国粮农组织(FAO)推荐采用修改的Penm an-M on te ith方法。该文指出不需要收集长序列气象资料,而以随机样本建立学习速率和动量因子自适应的BP神经网络模型估算参考作物腾发量(ET0)的方法,并且与FAO推荐的Penm an-M on te ith法计算值对比分析,结果表明:利用随机样本建立的的BP神经网络模型可以很好的反映气象因子(最高温度、最低温度、最大湿度、最小湿度、净辐射和风速)与参考作物腾发量(ET0)的非线性函数映射关系,并且取得了良好的估算效果,给出了国家自然科学基金重点项目研究区内蓝旗试验站2004年的时间尺度为日、十日参考作物腾发量(ET0)的计算及对比分析过程。展开更多
During environment testing, the estimation of random vibration signals (RVS) is an important technique for the airborne platform safety and reliability. However, the available meth- ods including extreme value envel...During environment testing, the estimation of random vibration signals (RVS) is an important technique for the airborne platform safety and reliability. However, the available meth- ods including extreme value envelope method (EVEM), statistical tolerances method (STM) and improved statistical tolerance method (ISTM) require large samples and typical probability distri- bution. Moreover, the frequency-varying characteristic of RVS is usually not taken into account. Gray bootstrap method (GBM) is proposed to solve the problem of estimating frequency-varying RVS with small samples. Firstly, the estimated indexes are obtained including the estimated inter- val, the estimated uncertainty, the estimated value, the estimated error and estimated reliability. In addition, GBM is applied to estimating the single flight testing of certain aircraft. At last, in order to evaluate the estimated performance, GBM is compared with bootstrap method (BM) and gray method (GM) in testing analysis. The result shows that GBM has superiority for estimating dynamic signals with small samples and estimated reliability is proved to be 100% at the given confidence level.展开更多
文摘参考作物腾发量(ET0)是计算作物需水量、制定灌溉制度和进行水资源管理的主要参数之一。计算参考作物腾发量(ET0)的方法众多,为规范ET0的求法,联合国粮农组织(FAO)推荐采用修改的Penm an-M on te ith方法。该文指出不需要收集长序列气象资料,而以随机样本建立学习速率和动量因子自适应的BP神经网络模型估算参考作物腾发量(ET0)的方法,并且与FAO推荐的Penm an-M on te ith法计算值对比分析,结果表明:利用随机样本建立的的BP神经网络模型可以很好的反映气象因子(最高温度、最低温度、最大湿度、最小湿度、净辐射和风速)与参考作物腾发量(ET0)的非线性函数映射关系,并且取得了良好的估算效果,给出了国家自然科学基金重点项目研究区内蓝旗试验站2004年的时间尺度为日、十日参考作物腾发量(ET0)的计算及对比分析过程。
基金supported by Aviation Science Foundation of China (No. 20100251006)the Technological Foundation Project (No. J132012C001)
文摘During environment testing, the estimation of random vibration signals (RVS) is an important technique for the airborne platform safety and reliability. However, the available meth- ods including extreme value envelope method (EVEM), statistical tolerances method (STM) and improved statistical tolerance method (ISTM) require large samples and typical probability distri- bution. Moreover, the frequency-varying characteristic of RVS is usually not taken into account. Gray bootstrap method (GBM) is proposed to solve the problem of estimating frequency-varying RVS with small samples. Firstly, the estimated indexes are obtained including the estimated inter- val, the estimated uncertainty, the estimated value, the estimated error and estimated reliability. In addition, GBM is applied to estimating the single flight testing of certain aircraft. At last, in order to evaluate the estimated performance, GBM is compared with bootstrap method (BM) and gray method (GM) in testing analysis. The result shows that GBM has superiority for estimating dynamic signals with small samples and estimated reliability is proved to be 100% at the given confidence level.