灰色模型在惯性平台误差系数预测中的应用

Error Coefficient Forecast of Inertial Platform Based on Grey Model

针对惯性平台误差系数呈现小样本和变化趋势多样化的特点,提出改进传统GM(1,1)模型中背景值的构造方式和原始数据算子变换的方法。背景值重构方法可以从结构上解决"以直代曲"引起的误差问题;算子变换方法可以弱化原始数据的随机性并增强其单调性,使得到的改进型GM(1,1)模型数据适用范围更广、模拟预测精度更高。通过实例分析,在两种不同趋势数据的情况下,改进型较传统型模拟预测结果精度都更高,曲线拟合程度更好,更能准确反映惯性平台误差系数的变化趋势。

Aiming at the characteristics of small sample and multiple variation trends of inertial platform error coefficient, an improved GM （ 1, 1 ） model is proposed. The model uses the improved background value and operator transformation of original data. The error caused by the ＂straight instead of curving＂ is eliminated by the method of background value reconstruction. Operator transformation method can weaken the randomness of the original data and enhance the monotonicity of it. Therefore, the improved GM （ 1, 1 ） model has a wider scope of application and higher forecast accuracy. Instance analysis result shows that： Compared with the traditional models, the improved model has higher accuracy of forecast and better fitting degree, which is more accurate to reflect the variation trend of inertial platform error coefficient.