A novel intelligent strategy for improving measurement precision of FOG

作者：Zhu, R^*; Zhang, YH; Bao, QL

来源：IEEE Transactions on Instrumentation and Measurement, 2000, 49(6): 1183-1188.

DOI：10.1109/19.893253

• biasing drift elimination
• FOG
• learning algorithm
• neural network
• noise elimination

This paper discusses neural network-based strategy for reducing the existing errors of fiber-optic gyroscope (FOG), A series-single-layer neural network, which is composed of two single-layer networks in series, is presented for eliminating random noises. This network has simpler architecture, faster learning speed, and better performance compared to conventional backpropagation (BP) networks. Accordingly, after considering the characteristics of the power law noise in FOG, an advanced learning algorithm is proposed by using the increments of errors in energy function. Furthermore, a radial basis RBF) neural network-based method is also posed to evaluate and compensate the temperature drift of FOG. The orthogonal least squares (OLS) algorithm is applied due to its simplicity, high accuracy, and fast learning speed. The simulation results show that the series-single-layer network (SSLN) with the advanced learning algorithm provides a fast and effective way for eliminating different random noises including stable and unstable noises existing in FOG, and the RBF network-based method offers a powerful and successful procedure for evaluating and compensating the temperature drift.

• 出版日期2000-12
• 单位清华大学; 上海交通大学