传统的径向基神经网络(RBFNN)在激光陀螺零偏的温度补偿过程中会由于随机选取中心不合适而导致算法效率降低和数值病态, 故本文提出了一种基于Kohonen网络和正交最小二乘(OLS)算法的RBFNN温度补偿方法。介绍了该方法的原理及建模步骤, 设计了常温和变温环境下激光陀螺的数据采集试验及其温度补偿试验。由于结合了Kohonen网络的模式分类能力和OLS的优化选择能力, 该方法可以快速、准确地辨识出受温度影响的激光陀螺零偏。利用逐步回归法、RBFNN法及其改进方法对多种温变环境影响的激光陀螺零偏进行了辨识与补偿试验, 试验结果表明, 在常温环境下, 三者的辨识能力相当;随着温变速率的上升, 改进RBFNN法不仅节省了时间, 其补偿后的零偏也均小于5×10-4(°)/h(1σ), 提高精度均能达86%以上。得到的结果表明改进RBFNN法提高了辨识精度且稳定、有效, 适用于多种温度变化环境下激光陀螺零偏的温度补偿。

When the Radial Basis Function Neural Network (RBFNN)is used for the temperature compensation of a laser gyro bias, it shows lower computing efficiency and numerical pathology due to incorrecting selection of an initial center randomly. Therefore, this paper proposes a new RBFNN method based on the Kohonen network and Orthogonal Least Squares (OLS) algrithm. It introduces the principle and modeling steps of the method and designs data collection and temperature compensation experiments of the laser gyro under normal temperature and variable temperature environments. As the method combines the pattern classification capability of the Kohonen network and the optimal choice capacity of the OLS, it avoids the effect of drawback mentioned above, and can quickly and accurately identify the laser gyro bias affected by temperatures. The identification and compensation tests for the laser gyro bias effected by a variety of temperature change situations are performed by the stepwise regression method, RBFNN method and the proposed modified methods in this paper. The test results show that the three methods all have the abilities to identify fairly in the situation of normal temperature;with increasing the rate of temperature change, proposed RBFNN method not only saves time, the compensated laser gyro bias is all also less than 5×10-4(°)/h (1σ), and its accuracy is improved more than 86%. The proposed RBFNN method enhances the stability and effectiveness of identification accuracy, and is suitable for laser gyro bias temperature compensation in a variety of temperature change conditions.