An adaptive nonlinear filter structure

Author

Matthews, Michael B.

Author_Institution

Swiss Federal Inst. of Technol., Zurich, Switzerland

fYear

1991

fDate

11-14 Jun 1991

Firstpage

694

Abstract

A simple, nonlinear, state-space model based on saturating nonlinearities is presented and shown to be dense in the set of all fading-memory systems, i.e., it can uniformly approximate a fading-memory system over all time. The approximation can be made arbitrarily close by increasing the order of the model. Furthermore, the model has good stability and observability properties. The state and parameters of the approximation are estimated using the recursive prediction error algorithm

Keywords

adaptive filters; filtering and prediction theory; observability; stability; state-space methods; adaptive nonlinear filter structure; approximation; fading-memory systems; observability; recursive prediction error algorithm; saturating nonlinearities; stability; state-space model; Filtering; Information processing; Nonlinear filters; Observability; Prediction algorithms; Recursive estimation; Signal processing; Stability; State estimation; State-space methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1991., IEEE International Sympoisum on

Print_ISBN

0-7803-0050-5

Type

conf

DOI

10.1109/ISCAS.1991.176429

Filename

176429

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2828535