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Adaptive parameter robust estimation
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008In this paper, we describe an adaptive technique for states and parameter estimation involving a combination of two methods, namely the Variable Structure Filter (VSF) and the Extend Kalman Filters (EKF).
Dhafar S. Mohammed +2 more
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Robust Estimation and Robust Parameter
2020This chapter is addressed to the problem of defining the parameter in a semiparametric situation. Suppose, for example, that the observation X is assumed to be expressed as \(X=\theta +\varepsilon \), where \(\theta \) is the parameter to be estimated and \(\varepsilon \) is the error whose distribution is not specified by a finite number of parameters.
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2011
In the previous chapters, we assumed that: 1. The true plant model and the estimated plant model have the same structure (the true plant model is described by a discrete time model with known upper bounds for the degrees n A , n B + d). 2. The disturbances are zero mean and of stochastic nature (with various assumptions). 3.
Ioan Doré Landau +3 more
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In the previous chapters, we assumed that: 1. The true plant model and the estimated plant model have the same structure (the true plant model is described by a discrete time model with known upper bounds for the degrees n A , n B + d). 2. The disturbances are zero mean and of stochastic nature (with various assumptions). 3.
Ioan Doré Landau +3 more
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Robust Confidence Intervals for Regression Parameters
Australian & New Zealand Journal of Statistics, 1998The paper considers the problem of finding accurate small sample confidence intervals for regression parameters. Its approach is to construct conditional intervals with good robustness characteristics. This robustness is obtained by the choice of the density under which the conditional interval is computed.
Field, Christopher A., Welsh, A. H.
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Robust controller for uncertain parameters systems
ISA Transactions, 2012In this paper, we present the synthesis of a robust controller for Linear Time Invariant (LTI) uncertain systems. A linear parametric uncertainties model is used to describe the system dynamic behavior. The main purpose of this controller is to guarantee some step response performances such as the settling time and the overshoot.
Maher, Ben Hariz +2 more
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Robust stability: parameter-dependent perturbations
International Journal of Control, 1983Conditions for a system to be robustly stable with respect to a set of perturbations, parametrized by possibly uncertain parameters, are given in terms of some ‘worst-case’ estimates of the perturbations. Making use of the Hadamard product of matrices, a new way of formulating perturbations is introduced. The advantage of representing the perturbations
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Choosing a robustness tuning parameter
Journal of Statistical Computation and Simulation, 2005A novel method is proposed for choosing the tuning parameter associated with a family of robust estimators. It consists of minimising estimated mean squared error, an approach that requires pilot estimation of model parameters. The method is explored for the family of minimum distance estimators proposed by [Basu, A., Harris, I.R., Hjort, N.L.
J. Warwick, M. C. Jones
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2012
The problem of robust parameter estimation in image registration is discussed and various robust methods for estimating registration parameters under outliers and inaccurate correspondences are reviewed and compared. After reviewing ordinary least-squares and weighted least-squares estimation, robust estimators such as maximum likelihood (M), repeated ...
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The problem of robust parameter estimation in image registration is discussed and various robust methods for estimating registration parameters under outliers and inaccurate correspondences are reviewed and compared. After reviewing ordinary least-squares and weighted least-squares estimation, robust estimators such as maximum likelihood (M), repeated ...
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Robustness in parameter estimation
IEEE Transactions on Information Theory, 1977A constructive approach to robust parameter estimation that carries over naturally to the nonparametric estimation is presented. Vagueness in previous notions of "robustness" has prevented such a connection from being made. To eliminate vagueness, robustness is defined in a precise mathematical way that leads to isolation of constructive analytical ...
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2014
In the field of statistical pattern recognition one is always faced with the problem to robustly estimate the parameters of a desired model on the available training samples. Consequently, robust parameter estimation is a primary problem when applying HMMs in practice.
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In the field of statistical pattern recognition one is always faced with the problem to robustly estimate the parameters of a desired model on the available training samples. Consequently, robust parameter estimation is a primary problem when applying HMMs in practice.
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