Genetic Tuning on Fuzzy Linguistic 2-Tuples Systems Based on the Representation
Rafael Alcali, Francisco Herrera · 2004
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 itzterpre&abiii@ of the obtained model are contradictory properties directly depending on the learning pro- cess and/or the model structure. Thus, the necessity of improving the linguistic model eecuracy 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 b) means of an eToIutionar3 lateral tuning of the linguistic variables, with the main aim of obtaining Fuzzy Rule-Based Systems with a better accuracy and maintain- ing a good interpretability. To do so, this tuning considers a new rule representation scheme bq 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.