System identification using a linear combination of cumulant slices
System identification using a linear combination of cumulant slices
复制标题
使用累积切片的线性组合进行系统辨识
DOI：
10.1109/78.224249
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发表时间：
1993
期刊：
影响因子：
--
通讯作者：
J. Vidal
中科院分区：
文献类型：
--
作者：
José A. R. Fonollosa;J. Vidal
提出了一种从输出统计量中辨识滑动平均（MA）模型参数的线性方法。首先，证明了在一定的约束条件下，系统的脉冲响应可以表示为累积量切片的线性组合。然后，这个结果被用来获得一个良好的条件线性方法估计的MA参数的非高斯过程。用于计算MA参数的切片的线性组合可以从不同阶数的不同累积量集合构造，提供了一个可以组合所有统计量的一般框架。它是没有必要使用二阶统计量（自相关切片），因此，该算法仍然提供了一致的估计存在有色高斯噪声。该方法的另一个优点是，虽然大多数线性方法给出完全错误的估计，如果阶被高估，所提出的方法不需要滤波器阶的先前估计。仿真结果证实了良好的数值条件的算法和其性能的改善，与现有的方法相比。>
A linear approach to identifying the parameters of a moving-average (MA) model from the statistics of the output is presented. First, it is shown that, under some constraints, the impulse response of the system can be expressed as a linear combination of cumulant slices. Then, this result is used to obtain a well-conditioned linear method for estimating the MA parameters of a nonGaussian process. The linear combination of slices used to compute the MA parameters can be constructed from different sets of cumulants of different orders, provided a general framework in which all the statistics can be combined. It is not necessary to use second-order statistics (autocorrelation slice), and therefore the proposed algorithm still provides consistent estimates in the presence of colored Gaussian noise. Another advantage of the method is that while most linear methods give totally erroneous estimates if the order is overestimated, the proposed approach does not require a previous estimation of the filter order. The simulation results confirm the good numerical conditioning of the algorithm and its improvement in performance in comparison to existing methods. >