Parameter estimation of moving average processes using cumulants and nonlinear optimization algorithms
Parameter estimation of moving average processes using cumulants and nonlinear optimization algorithms
复制标题
使用累积量和非线性优化算法的移动平均过程的参数估计
DOI：
10.5220/0001182900110015
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
G. Favier
中科院分区：
文献类型：
--
作者：
M. Boulouird;M. Hassani;G. Favier
提出了一种用于MA模型盲辨识的非线性优化算法，即梯度下降法和高斯-牛顿法。利用含噪系统输出的三阶和四阶累积量与MA参数之间的关系，建立了一组非线性方程，并用上述两种非线性优化算法进行了求解。给出了仿真结果，比较了所提算法的性能。
In this paper nonlinear optimization algorithms, namely the Gradient descent and the Gauss-Newton algorithms, are proposed for blind identification of MA models. A relationship between third and fourth order cumulants of the noisy system output and the MA parameters is exploited to build a set of nonlinear equations that is solved by means of the two nonlinear optimization algorithms above cited. Simulation results are presented to compare the performance of the proposed algorithms.