Tracking time-varying coefficient-functions | Henrik Aalborg Nielsen, Torben Skov Nielsen, Alfred Karsten Joensen, Henrik Madsen, Jan Holst
| | Abstract | A method for adaptive and recursive estimation in a class of
non-linear autoregressive models with external input is proposed.
The model class considered is conditionally parametric ARX-models
(CPARX-models), which is conventional ARX-models in which the
parameters are replaced by smooth, but otherwise unknown,
functions of a low-dimensional input process. These
coefficient-functions are estimated adaptively and recursively
without specifying a global parametric form, i.e.\ the method
allows for on-line tracking of the coefficient-functions.
Essentially, in its most simple form, the method is a combination
of recursive least squares with exponential forgetting and local
polynomial regression. It is argued, that it is appropriate to
let the forgetting factor vary with the value of the external
signal which is the argument of the coefficient-functions. Some
of the key properties of the modified method are studied by
simulation. | | Keywords | Adaptive and recursive estimation; Non-linear models; Time-varying functions; Conditional parametric models; Non-parametric method. | | Type | Journal paper [With referee] | | Journal | International Journal of Adaptive Control and Signal Processing | | Year | 2000 Vol. 14 No. 8 pp. 813-828 | | Electronic version(s) | [ps] | | BibTeX data | [bibtex] | | IMM Group(s) | Mathematical Statistics |
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