预报值 meaning in Chinese
forecast value
predicted value
Examples
- Residuals : generally , the difference between a measured value and the value predicted from a model
偏差：通常指测量值和某典型的预报值之间的差值。 - Both parameters and observed values are considered as grey in dam safety monitoring models . grey parameters are identified by the means of the grey system theory and then forecasting values are given hi the format of grey interval . 4
将大坝安全监控模型中的参数和实测数据均视为灰色，利用灰色系统方法对灰参数进行了辨识，并对大坝的监测效应量给出了灰色区间预报值。 - To limit the predicting precision loss in a certain range , author presented a method of bayes modeling and predicting for dynamic errors based on standard value interpolation at intervals during the multi - step prediction after consulting a lot of papers at home and abroad
为将预报精度损失控制在一定的范围之内，作者在查阅了国内外大量相关文献之后，提出了基于标准量插入的动态测量误差的贝叶斯建模预报理论，并根据贝叶斯理论给出了预报值的不确定度。 - To determine influence of technical conditions on product diameter , the influences of concentrations , molar ratio , reaction temperature and time on average size were investigated by means of uniform design . the results of experiments indicated that product diameter was mostly influenced by reaction temperature , followed by molar ratio of reactant , concentration of reactants , and reaction time . the optimum conditions were c ( co ( no3 ) 2 6h2o ) = 0 . 35mol / l , n ( co ( nh2 ) 2 ) : n ( co ( no3 ) 2 6h2o ) = 3 . 53 : 1 , reaction temperature 94 and reaction time 3 . 53h
以产物的平均粒径( nm )为优化指标，选择硝酸钴浓度、反应物摩尔比、反应温度和反应时间四个因素，运用均匀试验设计技术进行3 ~ 5 1 ~ 3因素优化试验，发现反应温度对产物的粒径影响最大，其次分别为反应物摩尔比、硝酸钴浓度以及反应时间，并确定最佳反应条件为：硝酸钴浓度0 . 35mol l ，反应物摩尔比为3 . 53 ： 1 ，反应温度94 ，反应时间3 . 53h ，预报值粒径y _ 1 = 19 . 57nm ，在此优化条件下制得的纳米co _ 3o _ 4平均粒径为22nm 。 - In the final part of the paper , the feasibility of applying neural networks to evaluate the performance of the columns is investigated . a three - layer back - propagation network is trained using the earthquake - resistant behavior experimental data of the columns to predict the ductility of the columns . the predicted results agree well with the test results
在论文的最后，探索应用人工神经网络对核心柱的力学性能进行评估的可能性，利用该柱抗震性能试验的结果，训练一个三层bp网络，进行了柱抗震延性的预报，预报值和试验值吻合良好。