Temperature compensation for MEMS resonant accelerometer based on genetic algorithm optimized backpropagation neural network

Temperature compensation with high accuracy is crucial for improving the performance of MEMS resonant accelerometers. In this paper, we propose an effective temperature compensation method based on the backpropagation neural network (BP-NN). First, we analyzed the relationship among the input acceleration, the environmental temperature, the output frequencies, and the scale factor of a MEMS resonant accelerometer through the traditional polynomial fitting method. After that, we introduced the BP-NN improved by genetic algorithm (GA). Numerous experiments were performed to train the BP-NN model and establish the relationships between the input layer and the output layer. Comparison between single-beam working mode and symmetrical double-beam working mode of the MEMS resonant accelerometer proved that the latter had a better temperature compensation effect due to its minimized error caused by temperature measurement. Experimental results show that the maximum error of our approach is 0.017 % over the whole temperature range from -10°C to 80°C, which is 173-times better than the traditional polynomial fitting method.