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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 protocol for the estimation of parameters in process models: Case studies with polymerization scenarios
(Wiley-blackwellMaldenEUA, 2004)
Dynamic parameter estimation problem for a water quality model
(AIDIC, 2007-06)
This work deals with the parameter estimation problem for a lake eutrophication model. The model is a dynamic parameter estimation one, which is solved with a simultaneous approach with an nonlinear programming solver. ...
Interval analysis and optimization applied to parameter estimation under uncertainty
(Boletim da Sociedade Paranaense de Matematica, 2018-01-01)
We present a methodology through exemplification to perform parameter estimation subject to possible factors of uncertainty. The underlying optimization problem is posed in the framework of the theory of interval-valued ...
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 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 ...
Reduction of Models in the Presence of Nuisance Parameters
(UNIV NAC COLOMBIA, DEPT ESTADISTICA, 2009)
In many statistical inference problems, there is interest in estimation of only some elements of the parameter vector that defines the adopted model. In general, such elements are associated to measures of location and the ...
Estimation of hydraulic parameters under unsaturated flow conditions in heap leaching
(ELSEVIER SCIENCE BV, 2015)