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Manifold-constrained regressors in system identification
http://ieeexplore.ieee.org/Xplore/l...739302.pdf?arnumber=4739302&authDecision=-203
Ohlsson, H. Roll, J. Ljung, L.
Dept. of Electr. Eng., Linkoping Univ., Linkoping, Sweden
This paper appears in: Decision and Control, 2008. CDC 2008. 47th IEEE Conference on
Issue Date: 9-11 Dec. 2008
On page(s): 1364 - 1369
Location: Cancun
ISSN: 0191-2216
Print ISBN: 978-1-4244-3123-6
INSPEC Accession Number: 10442500
Digital Object Identifier: 10.1109/CDC.2008.4739302
Date of Current Version: 06 January 2009
ABSTRACT
High-dimensional regression problems are becoming more and more common with emerging technologies. However, in many cases data are constrained to a low dimensional manifold. The information about the output is hence contained in a much lower dimensional space, which can be expressed by an intrinsic description. By first finding the intrinsic description, a low dimensional mapping can be found to give us a two step mapping from regressors to output. In this paper a methodology aimed at manifold-constrained identification problems is proposed. A supervised and a semi-supervised method are presented, where the later makes use of given regressor data lacking associated output values for learning the manifold. As it turns out, the presented methods also carry some interesting properties also when no dimensional reduction is performed
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Extended T-S fuzzy model based on interval arithmetic and its application to interval nonlinear regression analysis
http://ieeexplore.ieee.org/Xplore/l...277348.pdf?arnumber=5277348&authDecision=-203
Sun Changping Xu Zhengguang
Key Lab. of Adv. Control for Iron & Steel Process, Univ. of Scinece & Technol. Beijing, Beijing, China
This paper appears in: Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on
Issue Date: 20-24 Aug. 2009
On page(s): 1773 - 1778
Location: Jeju Island
ISSN: 1098-7584
Print ISBN: 978-1-4244-3596-8
INSPEC Accession Number: 10905647
Digital Object Identifier: 10.1109/FUZZY.2009.5277348
Date of Current Version: 02 October 2009
ABSTRACT
In this paper, a new fuzzy system model structure - interval T-S fuzzy model (ITSFM) is proposed. Inspired from interval regression analysis, the interval arithmetic is incorporated with classical T-S fuzzy model and the parameters in consequent part of the ITSFM model become to be interval numbers. Thus, the outputs of the proposed ITSFM are interval numbers. In our ITSFM model, the membership functions are the same as the ones of the classical type. Finally, the proposed ITSFM is applied to interval nonlinear regression analysis with crisp inputs and interval outputs. Experimental results are then presented that indicate the validity and applicability of the proposed ITSFM.
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http://kewlfile.com/dl/101972397463/getPDF_2.pdf
http://kewlfile.com/dl/101972399900/getPDF1.pdf