Nonlinear autoregressive and nonlinear autoregressive moving average model parameter estimation by minimizing hypersurface distance

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 l...

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Veröffentlicht in:	IEEE transactions on signal processing 2003-12, Vol.51 (12), p.3020-3026
Hauptverfasser:	Sheng Lu, Chon, K.H.
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Applied sciences Autoregressive moving average Autoregressive processes Biological control systems Biological system modeling Biological systems Computer simulation Detection, estimation, filtering, equalization, prediction Exact sciences and technology Independent variables Information, signal and communications theory Least squares approximation Least squares method Mathematical models Noise Nonlinear dynamical systems Nonlinear systems Nonlinearity Parameter estimation Parametric statistics Signal and communications theory Signal to noise ratio Signal, noise Studies Telecommunications and information theory
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