摘要
针对时变自回归滑动平均(TVARMA)模型参数谱估计容易出现谱峰漂移的问题,提出一种基于组合目标函数和遗传算法的TVARMA模型参数估计方法,并将之应用于飞行器结构响应序列的谱估计。首先,通过长自回归方法和增广最小二乘方法获得TVARMA模型参数初始估计值;其次,依据连续函数极值条件推导模型参数的频域约束条件并结合罚函数方法构造组合目标函数;最后,采用遗传算法对模型参数进行优化获得使组合目标函数最小的参数值作为TVARMA模型参数的最优估计。应用结果表明:该方法可以克服谱峰漂移现象,提高模型在时域和时频域的建模精度。
To solve the problem of spectral deviation for Time-Varied Auto-Regressive Moving Average （TVARMA） parametric spectrum estimation, a TVARMA parameter estimation method based on Genetic Algorithm （GA） and combined objective function was proposed and then used for estimating the spectrum of flight vibration time series. Firstly, an initial estimation of the model parameters was acquired by the long time-varied auto-regressive and augmented Least-Square （LS） method; secondly, through extremum condition of continuous function, a constraint equation of model parameters was derived and a combined objective function was constructed based on the punishment function idea; lastly, the GA was used to optimize the initial parameters and the optimum parameters were the ones that made the combined objective function be the least. The experimental results demonstrate that the new method can overcome the spectrum deviation and improve the model precision both in time domain and frequency domain.
出处
《计算机应用》
CSCD
北大核心
2010年第11期3097-3100,共4页
journal of Computer Applications
关键词
时变自回归滑动平均模型
增广最小二乘估计
组合目标函数
遗传算法
谱估计
Time-Varied Auto-Regressive Moving Average （TVARMA） model
augmented Least-Square （LS） estimation
combined objective function
Genetic Algorithm （GA）
spectrum estimation
