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Identifying adverse event clusters and treatment effects in randomised trials using Latent Class Analysis: an application in the COV-BOOST trial

Khoueiry LM, Vogt EV, Janani L, Cornelius V, Fraust SN, Munro APS, Liu X, Cro S, group Cs.   +8 more
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SET MEMBERSHIP ESTIMATION OF NONLINEAR REGRESSIONS

IFAC Proceedings Volumes, 2002
Abstract In this paper we propose a method, based on a Set Membership approach, for the estimation of nonlinear regressions models. At the contrary of most of the existing identification approaches, the method presented in this paper does not need any assumption about the functional form of the model to be identified, but uses only some prior ...
MILANESE, Mario, NOVARA, Carlo
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Interval set‐membership estimation for continuous linear systems

International Journal of Robust and Nonlinear Control, 2020
SummaryThis article proposes a mixed interval set‐membership estimation (ISME) method for continuous linear time‐invariant (LTI) systems by combining the positive system theory and the set theory. The proposed ISME method gives a new mixed interval‐set estimation framework for continuous LTI systems, whose benefit consists in that it has potential to ...
Feng Xu, Songlin Yang, Bin Liang
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Nonlinear state estimation by Extended Parallelotope Set-Membership Filter

ISA Transactions, 2022
In this paper, we propose a state estimation method called the Extended Parallelotope Set-Membership Filter that provides a higher estimation accuracy than existing methods for discrete-time nonlinear systems. The Extended Parallelotope Set-Membership Filter is motivated by the fact that the iteration operations in existing methods generate much ...
Danyang Qu, Zheng Huang, Yiwen Zhao, Guoli Song, Kui Yi, Xingang Zhao   +5 more
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Attack detection based on set-membership estimation

2020 39th Chinese Control Conference (CCC), 2020
This paper investigates the attack detection problem for a class of uncertain time-varying systems with sensor saturation. The set-membership estimation approach is adopted to deal with the unknown-but-bounded (UBB) process and measurement noises.
Hao Liu, Xinrui Wang
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Power system set membership state estimation

2012 IEEE Power and Energy Society General Meeting, 2012
In this paper, a power system set membership state estimator (SMSE) in a bounded-error context is proposed based on interval constraint propagation. Its effectiveness is tested with the IEEE 4-bus, 14-bus, 30-bus, 118-bus, and 300-bus systems. Comparison is made between SMSE and typical existing methods. Simulation results show that the proposed method
null Junjian Qi, null Guangyu He, null Shengwei Mei, null Feng Liu   +3 more
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Weighted Average Errors in Set-Membership Estimation

Mathematics of Control, Signals, and Systems (MCSS), 2003
The author considers the average behavior of estimation algorithms based on corrupted information, with values in a subspace of the problem element space. The paper deals with an optimal algorithm and derives exact error formulae in Euclidean norms in problem element and information spaces.
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Set-membership estimation for adaptive signal processing

1991 IEEE International Symposium on Circuits and Systems (ISCAS), 1991
The authors give a brief overview of the problem of set-membership estimation in adaptive signal processing. Recently, there seems to be a resurgence of interest in these types of estimation schemes as they feature 100% confidence regions for parameter estimates. Some of these recursive schemes also feature selective updating.
A.K. Rao, Y.-F. Kuang
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Unfalsified weighted least squares estimates in set-membership identification

IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 1997
It is well known that the weighted least squares (WLS) identification algorithm provides estimates that are in general not in the membership set and in this sense are falsified estimates. This paper shows that: (1) if the noise bound is known, the WLS estimates can be made to lie in or converge to the membership set by choosing the weights properly and
Bai, EW, Qiu, L., Tempo, R.
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