Non-parametric nonlinear system identification: An asymptotic minimum mean squared error estimator

Er-Wei Bai

doi:10.1109/CDC.2009.5400648

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Conference proceeding

Non-parametric nonlinear system identification: An asymptotic minimum mean squared error estimator

Er-Wei Bai

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, pp.6768-6773

12/2009

DOI: 10.1109/CDC.2009.5400648

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Abstract

This paper studies the problem of the minimum mean squared error estimator for non-parametric nonlinear system identification. It is shown that for a wide class of nonlinear systems, the local linear estimator is a linear (in outputs) asymptotic minimum mean squared error estimator. The class of the systems allowed is characterized by a stability condition that is related to many well studied stability notions in the literature. Numerical simulations support the analytical analysis.

Asymptotic stability

Convergence

Finite impulse response filter

Kernel

Linear systems

Linearity

Nonlinear systems

Numerical simulation

Polynomials

System identification

Details

Title: Subtitle: Non-parametric nonlinear system identification: An asymptotic minimum mean squared error estimator
Creators: Er-Wei Bai - University of Iowa
Resource Type: Conference proceeding
Publication Details: Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, pp.6768-6773
Publisher: IEEE
DOI: 10.1109/CDC.2009.5400648
ISSN: 0191-2216
Language: English
Date published: 12/2009
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984197114402771

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