NDLI: Convex relaxations for robust identification of Wiener systems and applications

Content Provider	IEEE Xplore Digital Library
Author	Yilmaz, B. Ayazoglu, M. Sznaier, M. Lagoa, C.
Copyright Year	2011
Description	Author affiliation: Department of Electrical Engineering, Penn State University, University Park, 16802, USA (Lagoa, C.) \|\| Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115, USA (Yilmaz, B.; Ayazoglu, M.; Sznaier, M.)
Abstract	This paper considers the identification of Wiener systems in a worst case framework. Given some a priori information about the admissible set of plants, nonlinearities and measurement noise, and a posteriori experimental data, our goal is twofold: (i) establish whether the a priori and a posteriori information are consistent, and (ii) in that case find a model that interpolates the available experimental information within the noise level. As recently shown, this problem is generically NP hard both in the number of data points and the number of inputs to the non-linearity. Our main result shows that a computationally attractive relaxation can be obtained by recasting the problem as a rank-constrained semi-definite optimization and using existing tools specifically tailored to this type of problems. These results are illustrated with a practical application drawn from computer vision
Starting Page	2812
Ending Page	2818
File Size	535557
Page Count	7
File Format	PDF
ISBN	9781612848006
ISSN	07431546
e-ISBN	9781612848013
e-ISBN	9781612847993
DOI	10.1109/CDC.2011.6161114
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimization Vectors Noise Noise measurement Polynomials Data models Mercury (metals)
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation

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