Tracking time-varying-coefficient functions
| Autor: | Jan Holst, Torben Skov Nielsen, Alfred K. Joensen, Henrik Aalborg Nielsen, Henrik Madsen |
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| Rok vydání: | 2000 |
| Předmět: |
Recursive least squares filter
Polynomial regression Mathematical optimization Process (computing) μ operator Autoregressive model Control and Systems Engineering Simple (abstract algebra) Signal Processing Applied mathematics Computer Science::Symbolic Computation Electrical and Electronic Engineering Parametric equation Parametric statistics Mathematics |
| Zdroj: | International Journal of Adaptive Control and Signal Processing. 14:813-828 |
| ISSN: | 1099-1115 0890-6327 |
| DOI: | 10.1002/1099-1115(200012)14:8<813::aid-acs622>3.0.co;2-6 |
| Popis: | SUMMARY 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 coe$cient functions are estimated adaptively and recursively without specifying a global parametric form, i.e. the method allows for on-line tracking of the coe$cient 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 coe$cient functions. Some of the key properties of the modi"ed method are studied by simulation. Copyright ( 2000 John Wiley & Sons, Ltd. |
| Databáze: | OpenAIRE |
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