Genetic tuning on fuzzy systems based on the linguistic 2-tuples representation

Linguistic fuzzy modeling allows us to deal with the modeling of systems building a linguistic model clearly interpretable by human beings. However, in this kind of modeling the accuracy and the interpretability of the obtained model are contradictory properties directly depending on the learning process and/or the model structure. Thus, the necessity of improving the linguistic model accuracy arises when complex systems are modeled. To solve this problem, one of the research lines of this framework in the last years has leaded up to the objective of giving more accuracy to the linguistic fuzzy modeling, without losing the associated interpretability to a high level. In this work, a new post-processing method of fuzzy rule-based systems is proposed by means of an evolutionary lateral tuning of the linguistic variables, with the main aim of obtaining fuzzy rule-based systems with a better accuracy and maintaining a good interpretability. To do so, this tuning considers a new rule representation scheme by using the linguistic 2-tuples representation model which allows the lateral variation of the involved labels. As an example of application of these kinds of systems, we analyze this approach considering a real-world problem.