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首页> 外文期刊>IEEE Transactions on Signal Processing >Nonlinear Autoregressive and Nonlinear Autoregressive Moving Average Model Parameter Estimation by Minimizing Hypersurface Distance
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Nonlinear Autoregressive and Nonlinear Autoregressive Moving Average Model Parameter Estimation by Minimizing Hypersurface Distance

机译：最小化超曲面距离的非线性自回归和非线性自回归移动平均模型参数估计

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摘要

The least squares (LS) can be used for nonlinear autoregressive (NAR) and nonlinear autoregressive moving average (NARMA) parameter estimation. However, for nonlinear cases, the LS results in biased parameter estimation due to its assumption that the independent variables are noise free. The total least squares (TLS) is another method that can used for nonlinear parameter estimation to increase the accuracy of the LS because it specifically accounts for the fact that the independent variables are noise corrupted. TLS has its own limitations, however, mainly because it is difficult for the method to isolate noise from the signal components. We present a new method that is based on minimization of hypersurface distance for accurate parameter estimation for NAR and NARMA models. Computer simulation examples show that the new method results in far more accurate NAR and NARMA model parameter estimates than do either the LS and TLS, with noise that is either white or colored, and retains its high accuracy even when the signal-to-noise ratio (SNR) is as low as 10 dB.
机译：最小二乘法（LS）可用于非线性自回归（NAR）和非线性自回归移动平均值（NARMA）参数估计。但是，对于非线性情况，由于LS假设自变量没有噪声，因此会导致参数估计有偏差。总最小二乘（TLS）是另一种可用于非线性参数估计以提高LS准确性的方法，因为它特别考虑了自变量受到噪声破坏的事实。但是，TLS有其自身的局限性，主要是因为该方法很难将噪声与信号分量隔离开。我们提出了一种基于超曲面距离最小化的新方法，用于NAR和NARMA模型的准确参数估计。计算机仿真示例表明，与LS和TLS相比，该新方法所产生的NAR和NARMA模型参数估计要精确得多，并且噪声为白色或彩色，即使在信噪比高的情况下也能保持其高精度。 （SNR）低至10 dB。

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