Fiber optic gyroscope temperature drift modeling method by optimizing dynamic recurrent neural network through genetic algorithm

Abstract

The invention discloses a fiber optic gyroscope temperature drift modeling method by optimizing a dynamic recurrent neural network through a genetic algorithm. The fiber optic gyroscope temperature drift modeling method by optimizing the dynamic recurrent neural network through the genetic algorithm comprises the following steps of (1) initializing network parameters, and establishing an improved Elman neural network model; (2) obtaining a training and testing sample; (3) training an improved Elman neural network, and optimizing model parameters through the genetic algorithm; (4) outputting forecasts of an fiber optic gyroscope, and compensating errors. The output of the fiber optic gyroscope processed through a denoising algorithm is trained by introducing the improved Elman neural model with self-feedback connection weight, constant iterative optimization is carried out on the model parameters through the genetic algorithm, and the optimal model is obtained according to the magnitude of the errors of the model under different parameters. According to the fiber optic gyroscope temperature drift modeling method by optimizing the dynamic recurrent neural network through the genetic algorithm, the complexity of the algorithm is taken into consideration, the accuracy of the fiber optic gyroscope temperature drift model is improved, the application of the fiber optic gyroscope temperature drift model in engineering is expanded, and certain practical significance is achieved.

Description

technical field [0001] The invention belongs to the technical field of inertia, and relates to a gyroscope drift modeling method, in particular to a temperature drift modeling method of an optical fiber gyroscope in a temperature-changing working environment. Background technique [0002] As a new generation of optical sensors, fiber optic gyroscopes are more and more widely used in a large number of precision instrument systems due to their unique advantages. However, since the core components of the fiber optic gyroscope are sensitive to temperature, when the ambient temperature changes, the output signal of the fiber optic gyroscope will be accompanied by temperature drift errors. As one of the main error sources of FOG, temperature drift seriously restricts the accuracy of FOG. Therefore, it is necessary to adopt the method of temperature error modeling and compensation to eliminate it. According to the Shupe effect of the fiber optic gyroscope, the non-reciprocal phase...

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Problems solved by technology

However, these algorithms have certain limitations, which are manifested in higher requirements for sample data and processing speed.

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