Nodos Knime para ajustar modelos usando la biblioteca de clases BiCIAM

Taday  González Chaveco; Cynthia  Porras Nodarse; Alejandro  Rosete Suárez

Authors

Taday González Chaveco Universidad Tecnológica de La Habana "José Antonio Echeverría"
Cynthia Porras Nodarse Universidad Tecnológica de La Habana "José Antonio Echeverría"
Alejandro Rosete Suárez Universidad Tecnológica de La Habana "José Antonio Echeverría"

Keywords:

BiCIAM; Knime; model optimization; model adjustment.

Abstract

Many problems in real life can be expressed using a mathematical model to describe them, through expressions with parameters that must be adjusted to each specific situation. The adjustment of these models often implies the solution of an optimization problem that looks for the values of the parameters that minimize the difference between what the model indicates and the real data. At present, to solve optimization problems it can use metaheuristics, but the tools that are used generally require programming skills. BiCIAM is a class library that solves optimization problems using metaheuristics, but only people with programming skills can make use of it. In this work we propose the implementation of new nodes in Knime that optimize and adjust mathematical models, using BiCIAM. With these nodes, optimization models can be solved without the need for programming, non-linear predictive models can be adjusted and the data can be analyzed using the statistical nodes provided by Knime.

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