@conference {943781,
	title = {A fast genetic method for inducting linguistically understandable fuzzy models},
	booktitle = {Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)},
	volume = {3},
	year = {2001},
	month = {July},
	pages = {1559-1563 vol.3},
	abstract = {Fuzzy rule bases can be regarded as mixtures of experts, and boosting techniques can be applied to learn them from data. In particular, provided that adequate reasoning methods are used, fuzzy models are extended additive models, thus backfitting can be applied to them. We propose to use an implementation of backfitting that uses a genetic algorithm for fitting submodels to residuals and we also show that it is both more accurate and faster than other fuzzy rule learning methods.},
	keywords = {Artificial intelligence, backfitting, boosting techniques, computational linguistics, extended additive models, fast genetic method, fuzzy logic, fuzzy models, Fuzzy reasoning, fuzzy rule bases, fuzzy rule learning methods, fuzzy set theory, Fuzzy sets, genetic algorithm, genetic algorithms, inference mechanisms, learning (artificial intelligence), Learning systems, linguistically understandable fuzzy model induction, mixtures of experts, reasoning methods, residuals, submodel fitting, Training data, uncertainty handling},
	doi = {10.1109/NAFIPS.2001.943781},
	author = {L. S{\'a}nchez}
}
@conference {943783,
	title = {Genetic tuning of fuzzy rule-based systems integrating linguistic hedges},
	booktitle = {Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)},
	volume = {3},
	year = {2001},
	month = {July},
	pages = {1570-1574 vol.3},
	abstract = {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.},
	keywords = {Computer 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},
	doi = {10.1109/NAFIPS.2001.943783},
	author = {J. Casillas and O. Cord{\'o}n and F. Herrera and M. J. del Jesus}
}
