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中文摘要：
本文通过择优RBF（径向基函数，Radial Basis Function）神经网络对影响切削加工过程的切削参数进行建模，对切除率进行拟合预测；提出松弛误差作为衡量网络精度的指标，使RBF选择最优的分布密度，从而有效提高RBF神经网络的拟合预测能力；并将择优RBF的拟合和预测结果与BP的相应结果进行了比较，结果显示择优RBF神经网络的拟合和预测精度大大优于BP神经网络。
英文摘要：
With Optimization-Making RBF Neural Network the excision rate was fitted and predicted via building on cutting parameters, which affecting cutting machining process. By proposing slack error as the indicator of the network＇s accuracy, radial basis function was made to select the best optimized distribution density in order to advance the fitting and forecasting capability of RBF Neural Network. The result of Optimization-Making RBF was compared with BP Neural Network＇s, what showed that the fitting and forecasting accuracy with Optimization-Making RBF was much higher than the accuracy with BF Neural Network.
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