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Time Series Decomposition using Automatic Learning Techniques for Predictive Models
(Institute of Physics Publishing, 2020-01-07)
This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to ...
Piecewise Linear Models in Model Predictive Control
(Planta Piloto de Ingeniería Química, 2001-12)
In this paper an efficient algorithm for Nonlinear Model Predictive Control is presented. The nonlinear problem is written as a simple Quadratic Programming problem with constaints by using a Canonical Piecewise Linear ...
Development of a neural network model to predict distortion during the metal forming process by line heating
(2013-01)
In order to achieve automation of the plate forming process by line heating, it is necessary to know in advance the deformation to be obtained under specific heating conditions. Currently, different methods exist to predict ...
Robust Identification of PWL-Wiener Models: Use in Model Predictive Control
(Planta Piloto de Ingeniería Química, 2003-06)
In this paper a robust identification strategy for Wiener like models constituted by a linear dynamic block a in series with a Piecewise Linear (PWL) functions as the nonlinear static gain is presented. The proposed ...
Fuzzy model-based predictive control for a CSTR with multiple steady state: A simulation study and a comparison with other nonlinear MBPC control algorithms
(World Scientific and Engineering Academy and Society, 2004-04)
In the paper a comparison of different nonlinear model-based predictive control algorithms is presented as a case study for a continuous stirred reactor. The focus is given to the fuzzy predictive control approach which ...
Efficient solution of nonlinear model predictive control by a restricted enumeration method
(Universidad UTE, 2018)