非最小相位ARMA模型的一種自適應(yīng)辨識算法
AN ADAPTIVE IDENTIFICATION ALGORITHM FOR NONMINIMUM PHASE ARMA MODELS
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摘要: 本文提出了一種加性有色高斯噪聲中因果非最小相位ARMA模型的自適應(yīng)辨識算法。模型輸入假定為非高斯獨(dú)立同分布隨機(jī)過程。算法只利用了觀測信號的高階統(tǒng)計量。在每次迭代中,先估計AR參數(shù),再估計MA參數(shù),但不用計算殘差序列。在參數(shù)遞推中采用了隨機(jī)梯度法。仿真實(shí)驗(yàn)證實(shí)了本文算法的有效性。Abstract: This paper proposes an adaptive identification algorithm for nonminimum phase ARMA models in additive colored Gaussian noise. The model input is assumed to be an i. i. d., non-Gaussian random process. The algorithm utilizes higher-order statistics of the observed signal alone. It estimates the AR and MA parameters successively in each iteration without computing the residual time series. The stochastic gradient method is used in parameter updating. Simulation resutls show the effectiveness of the algorithm.
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