Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/102022
Title: In system identification, interval (and fuzzy) estimates can lead to much better accuracy than the traditional statistical ones: General algorithm and case study
Authors: Kumkov, S. I.
Kreinovich, V.
Pownuk, A.
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Kumkov S. I. In system identification, interval (and fuzzy) estimates can lead to much better accuracy than the traditional statistical ones: General algorithm and case study / S. I. Kumkov, V. Kreinovich, A. Pownuk. — DOI 10.1109/SMC.2017.8122631 // 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. — 2017. — Vol. 2017-January. — P. 367-372.
Abstract: In many real-life situations, we know the upper bound of the measurement errors, and we also know that the measurement error is the joint result of several independent small effects. In such cases, due to the Central Limit Theorem, the corresponding probability distribution is close to Gaussian, so it seems reasonable to apply the standard Gaussian-based statistical techniques to process this data - in particular, when we need to identify a system. Yes, in doing this, we ignore the information about the bounds, but since the probability of exceeding them is small, we do not expect this to make a big difference on the result. Surprisingly, it turns out that in some practical situations, we get a much more accurate estimates if we, vice versa, take into account the bounds - and ignore all the information about the probabilities. In this paper, we explain the corresponding algorithms. and we show, on a practical example, that using this algorithm can indeed lead to a drastic improvement in estimation accuracy. © 2017 IEEE.
Keywords: CYBERNETICS
MEASUREMENT ERRORS
CENTRAL LIMIT THEOREM
GAUSSIANS
KNOW-THAT
STATISTICAL TECHNIQUES
UPPER BOUND
PROBABILITY DISTRIBUTIONS
URI: http://hdl.handle.net/10995/102022
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85044415538
PURE ID: 7014142
364c098e-501c-4189-9cf6-a2f4f3dfa1df
ISBN: 9781538616451
DOI: 10.1109/SMC.2017.8122631
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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