IDENTIFICATION OF HAMMERSTEIN SYSTEMS WITH QUANTIZED OBSERVATIONS

This work is concerned with identification of Hammerstein systems whose outputs are measured by quantized sensors. The system consists of a memoryless nonlinearity that is polynomial and possibly noninvertible, followed by a linear subsystem. The parameters of linear and nonlinear parts are unknown...

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Bibliographic Details
Published in:SIAM journal on control and optimization 2010-01, Vol.48 (7-8), p.4352-4376
Main Authors: YANLONG ZHAO, ZHANG, Ji-Feng, LE YI WANG, YIN, G. George
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Asymptotic properties
Computer science
control theory
systems
Consistency
Control theory. Systems
Design engineering
Distribution functions
Exact sciences and technology
Linear equations
Linear systems
Modelling and identification
Nonlinear systems
Nonlinearity
Optimization
Parameter identification
Sensors
Studies
System theory
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Summary:This work is concerned with identification of Hammerstein systems whose outputs are measured by quantized sensors. The system consists of a memoryless nonlinearity that is polynomial and possibly noninvertible, followed by a linear subsystem. The parameters of linear and nonlinear parts are unknown but have known orders. Input design, identification algorithms, and their essential properties are presented under the assumptions that the distribution function of the noise is known and the quantization thresholds are known. The concept of strongly scaled full rank signals is introduced to capture the essential conditions under which the Hammerstein system can be identified with set-valued observations. Under strongly scaled full rank conditions, a strongly convergent algorithm is constructed. Asymptotic consistency and efficiency of the algorithm are investigated. [PUBLICATION ABSTRACT]
ISSN:0363-0129
1095-7138
DOI:10.1137/070707877