Article ID Journal Published Year Pages File Type
4974512 Journal of the Franklin Institute 2017 27 Pages PDF
Abstract
This paper presents a bias compensation principle based recursive least squares (BCP-RLS) identification method for Hammerstein nonlinear autoregressive moving average with exogenous variable (ARMAX) systems. By introducing a non-singular matrix and an auxiliary vector uncorrelated with the noise term, we firstly establish the BCP-RLS unified framework. Next the convergence and consistency properties of the achieved BCP-RLS method are rigorously analyzed without the martingale difference sequence assumption or the strictly positive real condition. Furthermore, some discussions on the flexibility of the BCP-RLS method and its comparisons with some other existing methods are presented. Finally, some representative simulation examples are conducted to verify the obtained results.
Related Topics
Physical Sciences and Engineering Computer Science Signal Processing
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