<Previous Article In Issue
Volume 5, Issue 2, April 2012, Pages 387 - 402
Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling
Authors
Ángela Nebot, Francisco Mugica, Félix Castro, Jesús Acosta
Corresponding Author
Ángela Nebot
Received 15 October 2010, Accepted 1 June 2011, Available Online 1 April 2012.
- DOI
- 10.1080/18756891.2012.685328How to use a DOI?
- Keywords
- Genetic fuzzy systems, fuzzy inductive reasoning, predictive models, decision support models, e-learning
- Abstract
In this research a genetic fuzzy system (GFS) is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR) methodology and the Linguistic Rule FIR (LR-FIR) algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and useful predictive (FIR) models and decision support (LR-FIR) models. The GFS is evaluated in an e-learning context.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
<Previous Article In Issue
Cite this article
TY - JOUR AU - Ángela Nebot AU - Francisco Mugica AU - Félix Castro AU - Jesús Acosta PY - 2012 DA - 2012/04/01 TI - Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling JO - International Journal of Computational Intelligence Systems SP - 387 EP - 402 VL - 5 IS - 2 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2012.685328 DO - 10.1080/18756891.2012.685328 ID - Nebot2012 ER -