The improvement of predicting error is mainly due to introduction of general normalized elevation angle function.
引入通用归一化仰角函数是改善预报误差的主要原因。
It has also been validated to have high precision and reliability, the maximal single part predicting error is 4.8%.
通过试验验证了该系统的精度和可靠性，单项最大预测误差为4.8%。
The results show that BP artificial neural network can be used in predicting mechanical properties and grain size of materials after superplastic forming and its predicting error is less than 7%.
结果表明，BP神经网络用于材料超塑性变形后的力学性能及晶粒尺寸预测是可行的，其预测误差小于7%。
应用推荐