Adaptive BP Network Prediction Method for Ground Surface Roughness with High-Dimensional Parameters
Adaptive BP Network Prediction Method for Ground Surface Roughness with High-Dimensional Parameters
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DOI：
10.3390/math10152788
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发表时间：
2022-08
期刊：
影响因子：
2.4
通讯作者：
Xubao Liu;Yuhang Pan;Ying Yan;Yonghao Wang;Ping Zhou
中科院分区：
文献类型：
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作者：
Xubao Liu;Yuhang Pan;Ying Yan;Yonghao Wang;Ping Zhou
由于地表粗糙度影响因素复杂，难以通过物理模型进行预测。BP神经网络是一种很有前途的方法，在表面粗糙度预测中得到了广泛的应用。本文利用BP神经网络的概念，结合砂轮的状态，对磨削表面粗糙度进行预测。但是，随着输入参数个数的增加，模型的局部最优解产生的问题也越严重。因此，“识别因子”被设计来判断模型的迭代状态，而“记忆因子”被设计来存储网络训练过程中的最佳权值。改进了模型的迭代终止条件，调整了权值的学习速度和更新规则，避免了局部最优解。结果表明，该模型比传统模型具有更高的预测精度和更稳定的预测精度。在三种迭代步长下，平均预测精度分别从0.071、0.065、0.066提高到0.049、0.042、0.039，预测标准差分别从0.0017、0.0166、0.0175降低到0.0017、0.0070、0.0076。为提高BP神经网络的全局寻优能力和开发更精确的表面粗糙度预测模型提供了指导。
Ground surface roughness is difficult to predict through a physical model due to its complex influencing factors. BP neural networks (BPNNs), a promising method, have been widely applied in the prediction of surface roughness. This paper uses the concept of BPNN to predict ground surface roughness considering the state of the grinding wheel. However, as the number of input parameters increases, the local optimum solution of the model that arises is more serious. Therefore, “identify factors” are designed to judge the iterative state of the model, whilst “memory factors” are designed to store the best weights during network training. The iterative termination conditions of the model are improved, and the learning rate and update rules of the weights are adjusted to avoid the local optimal solution. The results show that the prediction accuracy of the presented model is higher and more stable than the traditional model. Under three types of iteration steps, the average prediction accuracy is improved from 0.071, 0.065, 0.066 to 0.049, 0.042, 0.039 and the standard deviation of prediction decreased from 0.0017, 0.0166, 0.0175 to 0.0017, 0.0070, 0.0076, respectively. Therefore, the proposed method provides guidance for improving the global optimization ability of BPNNs and developing more accurate models for predicting surface roughness.