Non-Parametric Nonlinear System Identification: An Asymptotic Minimum Mean Squared Error Estimator

Er-Wei BAI

doi:10.1109/TAC.2010.2042343

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Non-Parametric Nonlinear System Identification: An Asymptotic Minimum Mean Squared Error Estimator

Journal article

Peer reviewed

Non-Parametric Nonlinear System Identification: An Asymptotic Minimum Mean Squared Error Estimator

Er-Wei BAI

IEEE transactions on automatic control, Vol.55(7), pp.1615-1626

2010

DOI: 10.1109/TAC.2010.2042343

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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.

Applied Sciences

Mathematics

Statistics

Exact sciences and technology

Nonparametric inference

Control theory. Systems

Probability and statistics

Modelling and identification

Sciences and techniques of general use

Computer science; control theory; systems

Details

Title: Subtitle: Non-Parametric Nonlinear System Identification: An Asymptotic Minimum Mean Squared Error Estimator
Creators: Er-Wei BAI - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, United States
Resource Type: Journal article
Publication Details: IEEE transactions on automatic control, Vol.55(7), pp.1615-1626
Publisher: Institute of Electrical and Electronics Engineers
DOI: 10.1109/TAC.2010.2042343
ISSN: 0018-9286
eISSN: 1558-2523
Language: English
Date published: 2010
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984083828402771

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