@article{10272/23196,
year = {2013},
month = {12},
url = {https://hdl.handle.net/10272/23196},
abstract = {The use of adaptive connectors as conjunction operators in adaptive fuzzy inference systems is one of the methodologies, also compatible with others, to improve the accuracy of fuzzy rule-based systems by means of local adaptation of the inference process to each rule of the rule base. However, when dealing with such currently challenging issues as high-dimensional regression problems, adapting their parameters becomes difficult due to the exponential rule explosion.

In this paper, we propose to address the problem by using a new adaptive conjunction operator. This operator provides considerable advantages in efficiency while maintaining the accuracy. Moreover, it is completed with a multi-objective evolutionary algorithm as a search method due to its efficiency in achieving different balances between complexity and accuracy in the learned fuzzy systems.

An in-depth experimental study is performed to show the advantages of the proposal presented, using 17 regression problems of different size and complexity, using different rule bases, analyzing the multi-objective algorithms and Pareto fronts obtained and performing statistical analyses. It confirms its effectiveness in terms of efficiency, but also in terms of accuracy and complexity of the obtained models.},
organization = {Spanish Ministryof Economy and Competitiveness under Grant no. TIN2008-06681-C06-06},
publisher = {Elsevier},
title = {An efficient adaptive fuzzy inference system for complex and high dimensional regression problems in linguistic fuzzy modelling},
doi = {https://doi.org/10.1016/j.knosys.2013.05.012},
author = {Márquez Hernández, Antonio Ángel and Márquez Hernández, Francisco Alfredo and Roldán Ruiz, Ana María and Peregrín Rubio, Antonio},
}