In order to supress temperature errors of digital closed-loop fiber optic gyroscope (FOG)

a scheme based on radial basis function (RBF) neural networks was designed for temperature errors compensation and its applied scale factor error model and bias error model were investigated. First

based on the distribution of FOG’s temperature errors

a scheme

which combined scale factor error compensation and bias error compensation

was designed for temperature errors compensation. A multiscale analysis algorithm of signal feature extraction was used to preprocess original testing data for higher modeling accuracy. Then two RBF neural network models were developed and their learning algorithm was improved to avoid over-fitting. Finally

the effects of the models’ input vectors on the models’ scale are discussed as well. Analysis of simulation results indicate that residaul mean square (RMS) of the scale factor error model is 0.73 and the RMS of the bias error model is 0.051 . It can satisfy the requirements of real-time temperature compensation for middle and high precision FOG.