Abstract：The creep errors of the piezoelectric ceramics have nonlinear change with the time, which is difficult to revise in real time. A creep prediction approach based on back propagation neural network is proposed for the piezoelectric ceramics. The data is collected by the piezoelectric ceramic driving system and normalized for prediction. The parameters of BP neural network including the number of hidden layers, the number of nodes in each hidden layer, the node transfer functions and the training function are designed by experiments. The prediction model of BP neural network is established, and the connection between the creep of the piezoelectric ceramic and the time is built. The creep of piezoelectric ceramics is predicted by the model of BP neural network, compared with the measured data, the results show that, using this prediction model the maximal absolute error is below 0.1 μm, the maximal creep error is below 0.6% and the maximal mean square error is 0.0021. So the BP neural network prediction model has a high prediction accuracy and can be applied to the creep prediction of the piezoelectric ceramics.
范伟,林瑜阳,李钟慎. 基于BP神经网络的压电陶瓷蠕变预测[J]. 计量学报, 2017, 38(4): 429-434.
FAN Wei,LIN Yu-yang,LI Zhong-shen. Prediction of the Creep of Piezoelectric Ceramic Based on BP Neural Network Optimized by Genetic Algorithm. Acta Metrologica Sinica, 2017, 38(4): 429-434.
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2017, Vol. 38 
