Temperature drift modelling of fibre optic gyroscopes based on a grey radial basis function neural network

In this paper, a novel type of neural network called a grey radial basis function network (GRBFN), is addressed and applied to reduce the temperature influence on the output of fibre optic gyroscopes (FOGs) and to improve their performance. The reasons why grey theory is introduced into the RBF neural network are based on two facts. First, the output of FOGs is affected greatly by environmental factors, especially by environmental temperature variation, hence there will be large randomness on output data. Second, the modelling performance will be affected by the randomness inherent in the output data of FOGs when a neural network approach is applied to the model. That is, poor performance results from large randomness and vice versa. The grey accumulated generating operation (AGO), a basis of the grey theory, is reported to possess a randomness reduction property. Because of these facts, the GRBFN model is presented and expected to have better modelling precision of temperature drift in FOGs. The proposed GRBFN model consists of the grey AGO, the RBF neural network, and the grey inverse accumulated generating operation (IAGO). Raw data are first preprocessed by the grey AGO and then put into the RBF network to perform modelling. Modelling results of the GRBFN are then obtained from the grey IAGO. The numerical results of real drift data under different temperatures from a certain type of FOG verify the effectiveness of the proposed GRBFN model powerfully. The RBF neural network modelling approach is also investigated to provide a comparison with the GRBFN model. Under identical training conditions, the GRBFN's training speed has been enhanced greatly. It is shown that the proposed GRBFN model with different network structures outperforms the RBF network itself.

[1]  H J Shaw,et al.  Compensation of the optical Kerr effect in fiber-optic gyroscopes. , 1982, Optics letters.

[2]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[3]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[4]  H. Kajioka,et al.  Configuration to double I-FOG output power: Experimental investigation of noise performance and temperature effect , 1996, IEEE Photonics Technology Letters.

[5]  Cheng-Hsiung Hsieh.  Grey Neural Network and Its Application to Short Term Load Forecasting Problem , 2002 .

[6]  B Culshaw,et al.  Source statistics and the Kerr effect in fiber-optic gyroscopes. , 1982, Optics letters.

[7]  Saleem A. Kassam,et al.  Radial basis function networks in nonlinear signal processing applications , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[8]  R. Shorthill,et al.  Fiber ring interferometer. , 1976, Applied optics.

[9]  Paul Ruffin,et al.  Investigation of the effects of temporal thermal gradients in fiber optic gyroscope sensing coils, part 2 , 1995 .

[10]  Zhu Rong.  Identification of Temperature Drift for FOG Using RBF Neural Networks , 2000 .

[11]  Yuanhong Yang,et al.  Thermally induced error and compensating in open-loop fiber optical gyroscope , 2000, Optics and Optoelectronic Inspection and Control.

[12]  P. A. Moakes,et al.  Adaptive control using radial basis function networks , 1994 .

[13]  Yanhua Zhang,et al.  A novel intelligent strategy for improving measurement precision of FOG , 2000, IEEE Trans. Instrum. Meas..

[14]  Amado Cordova,et al.  Progress in navigation-grade IFOG performance , 1996, Optics + Photonics.