Application of a genetic algorithm Elman network in temperature drift modeling for a fiber-optic gyroscope.

The fiber-optic gyroscope (FOG) has been widely used as a satellite and automobile attitude sensor in many industrial and defense fields such as navigation and positioning. Based on the fact that the FOG is sensitive to temperature variation, a novel (to our knowledge) error-processing technique for the FOG through a set of temperature experiment results and error analysis is presented. The method contains two parts: one is denoising, and the other is modeling and compensating. After the denoising part, a novel modeling method which is based on the dynamic modified Elman neural network (ENN) is proposed. In order to get the optimum parameters of the ENN, the genetic algorithm (GA) is applied and the optimization objective function was set as the difference between the predicted data and real data. The modeling and compensating results indicate that the drift caused by the varying temperature can be reduced and compensated effectively by the proposed model; the prediction accuracy of the GA-ENN is improved 20% over the ENN.