摘要
武器系统故障数据为小样本灰色序列,时常呈现摆动特性,研究发现,灰色摆动序列的建模不满足GM(1,1)模型的条件。为此,提出首先利用动态指数变换,使灰色摆动序列变换为具有一定灰指数律的单调增序列,然后再建立GM(1,1)模型,称之为灰色线性幂函数曲线模型(GIM(1))。对于GIM(1)模型,利用一元线性回归优化建模法进行模型参数辨识。结果证明,GIM(1)模型对于武器系统故障序列中的灰色摆动序列具有良好的拟合和预测精度,不仅融合了灰色辨识算法的优点,而且也能满足一般系统的辨识要求。
Fault data of weapon systems are small-sample grey sequence, which often take on the wobbly characteristic. It is found by research that modeling of the grey wobbly sequence does not satisfy the condition of GM （1, 1 ） model. Therefore, it is proposed to use the dynamic exponent transformation for transforming the grey wobbly sequence into a monotonically increasing sequence with certain grey exponent law, and then to establish a GM （1, 1 ） model, which is called as grey linear power exponent function curve model （ GIM （ 1 ） ）. For GIM （ 1 ）, the unary linear regression modeling method is used for model parameter identification. The results prove that GIM （1） model has good fitting and prediction accuracy for the grey wobbly sequence of the weapon system＇s fault sequence, which not only has the advantage of grey identification algorithm, but also can meet the identification requirement of general system.
出处
《电光与控制》
北大核心
2015年第9期106-109,共4页
Electronics Optics & Control
关键词
故障预报
武器系统
灰色摆动序列
线性幂指数曲线
一元线性回归
fault prediction
weapon system
grey wobbly sequence
linear power exponent curve
unary linear regression
