压电陀螺漂移特性的灰色神经网络建模研究

Study on Gray Neural Network Drift Modeling for Piezoelectric Gyro

在不同温度和运行时间下,压电陀螺的静态零位漂移和刻度因子变化表现为非线性和多值对应关系,基于时间序列的ARMA模型和直接采用温度单输入的前馈神经网络模型都无法正确描述压电陀螺的这种误差特性。提出一种将温度和时间作为输入,分别同时对应陀螺静态零位和刻度因子输出的三维空间下的单值对应关系,在运用灰色理论对数据进行预处理基础上,采用多输入单输出的RBF神经网络实现了对陀螺漂移特性的描述。仿真实验结果验证了该建模方法的有效性。

The piezoelectric gyro＇s drift has a multi-valued nonlinear behavior in different temperature and operation time. It can not be described by using temperature input neural network model and time sequence model （ARMA）. A single-mapping based on the three dimension coordinates was presented. Temperature and run time were designed as input, gyro＇s stationary null voltage and scale factor were designed as output in the tree dimension coordinates. Grey accumulate operation （AGO） was used in the processing of acquired data. Then, the RBF neural network model was presented to approximate the gyro＇s drift. The simulation results show that the new approach for modeling is effective and of high precision.