优化结果表明，该组冲击参数的平均相对误差为1.3%，各单项误差也均满足小于5%的指标要求。
The optimized results showed that the average relative error of this combination is 1.3%, and the individual error is also able to meet the requirements of less than 5%.
分析了坐标测量机几何误差的几种常用模型，提出了基于神经网络的单项几何误差模型。
Several coordinate measuring machine geometry error models of several kinds in common use are analyzed in this paper.
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%。
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%。
It has also been validated to have high precision and reliability, the maximal single part predicting error is 4.8%.
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