Modeling temperature drift of FOG by improved BP algorithm and by Gauss-Newton algorithm

The large temperature drift caused by variation of environmental temperature is the main factor affecting the performance of fiber optical gyroscope (FOG). Based the advantages of artificial neural network and the fact that the temperature drift of FOG is a group of multi-variable non-line time series related with temperature, this paper presents modeling temperature drift of fiber optical gyro rate by improved back propagation (BP) training algorithm and by Gauss-Newton training algorithm, comparison between the modeling results of by improved BP algorithm and by gauss-newton algorithm is presented. Modeling results from measured temperature drift data of FOG shows that Gauss-Newton algorithm has higher training precision and shorter convergence time than improved BP algorithm on the same training conditions for application of modeling temperature drift of FOG.