Genetic tuning of fuzzy rule-based systems integrating linguistic hedges

TitleGenetic tuning of fuzzy rule-based systems integrating linguistic hedges
Publication TypeConference Paper
Year of Publication2001
AuthorsCasillas, J., Cordón O., Herrera F., and del Jesus M. J.
Conference NameProceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)
Pagination1570-1574 vol.3
Date PublishedJuly
KeywordsComputer science, experimental results, fuzzy logic, Fuzzy rule-based systems, Fuzzy sets, Fuzzy systems, generalisation (artificial intelligence), generalization, genetic algorithms, genetic tuning process, knowledge base, knowledge based systems, linguistic hedges, linguistic modeling, membership functions, Proposals, Shape, Takagi-Sugeno model, Timing, uncertainty handling

Tuning fuzzy rule-based systems for linguistic modeling is an interesting and widely developed task. It involves adjusting the membership functions composing the knowledge base. To do that, changing the parameters defining each membership function as using linguistic hedges to slightly modify them may be considered. This paper introduces a genetic tuning process for jointly making these two tuning approaches. The experimental results show that our method obtains accurate linguistic models in both approximation and generalization aspects.