Activation functions selection for BP neural network model of ground surface roughness

Roughness prediction of ground surfaces is critical in understanding and optimizing the grinding process. However, it is hitherto difficult to predict accurately the ground surface roughness by theoretical and empirical models due to the complexity of grinding process. BP neural network (BPNN), which can be used to establish the relationship between processing parameters and surface roughness, avoids the difficulty of revealing the complex physical mechanism and thus has unique potential in automatic optimization of grinding process in industrial practice. Activation function is one of the most important factors affecting the efficiency and accuracy of BPNN. Nevertheless, it is often selected arbitrarily or at most by trials or tuning. This paper proposes an activation function selection approach in which virtual data generated from the approximate physical model are employed to evaluate the performance of the BPNN in practice application. The results show that with tansig as the activation function of hidden layer and purelin as the activation function of output layer, the BPNN model can obtain the highest learning efficiency. Moreover, when the activation function of hidden layer is sigmoid, whose shape factor is 1–3, and the output layer activation function is purelin, the model can predict more precisely. Finally, the proposed approach is validated by comparing the performance of BPNN obtained from the virtual data and the experimental data. Obtained results showed that the proposed approach is a simple and effective way to determine the activation function of BPNN.