A Recursive Prediction Error Parameter Estimator For Nonlinear Models
Chen, S. and Billings, S.A.
(1988)
A Recursive Prediction Error Parameter Estimator For Nonlinear Models.
Research Report.
Acse Report 326
.
Dept of Automatic Control and System Engineering. University of Sheffield
Abstract
A recursive prediction error parameter estimation algorithm is derived for systems which can be represented by the NARMAX (Nonlinear ARMAX) model. A convergence analysis is presented using the differential equation approach and the new concept of m-invertibility is introduced. The analysis shows that while a highly nonlinear process model may be used to capture the nonlinearity of the system it is advisable to fit a simple noise model. The results of applying the algorithm to both simulated and real data are included.
Metadata
Item Type:
Monograph
Authors/Creators:
Chen, S.
Billings, S.A.
Copyright, Publisher and Additional Information:
he Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
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