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A neural network approach for robust nonlinear parameter estimation in presence of unknown-but-bounded errors
(Elsevier B.V., 2000-01-01)
Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. This paper presents a ...
A neural system to robust Nonlinear optimization subject to disjoint and constrained sets
(Int Inst Informatics & Systemics, 2001-01-01)
The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel method using artificial neural networks to solve robust parameter ...
A neural system to robust Nonlinear optimization subject to disjoint and constrained sets
(Int Inst Informatics & Systemics, 2001-01-01)
The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel method using artificial neural networks to solve robust parameter ...
A novel approach to robust parameter estimation using neurofuzzy systems
(Elsevier B.V., 1999-02-01)
A novel approach for solving robust parameter estimation problems is presented for processes with unknown-but-bounded errors and uncertainties. An artificial neural network is developed to calculate a membership set for ...
A novel approach to robust parameter estimation using neurofuzzy systems
(Elsevier B.V., 1999-02-01)
A novel approach for solving robust parameter estimation problems is presented for processes with unknown-but-bounded errors and uncertainties. An artificial neural network is developed to calculate a membership set for ...
A neural network approach for robust nonlinear parameter estimation in presence of unknown-but-bounded errors
(Elsevier B.V., 2000-01-01)
Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. This paper presents a ...
Semiparametric estimation and inference using doubly robust moment conditions
(2013-12-05)
We study semiparametric two-step estimators which have the same structure as parametric doubly robust estimators in their second step. The key difference is that we do not impose any parametric restriction on the nuisance ...
Robust tests for linear regression models based on τ-estimates
(Elsevier Science, 2016-01)
ANOVA tests are the standard tests to compare nested linear models fitted by least squares. These tests are equivalent to likelihood ratio tests, so they have high power. However, least squares estimators are very vulnerable ...
Robust estimators in semi-functional partial linear regression models
(Elsevier Inc, 2017-02)
Partial linear models have been adapted to deal with functional covariates to capture both the advantages of a semi-linear modelling and those of nonparametric modelling for functional data. It is easy to see that the ...