Genetic algorithms for fuzzy controllers
AI Expert
A simple but powerful heuristic method for generating fuzzy rules from numerical data
Fuzzy Sets and Systems
Applicability of the fuzzy operators in the design of fuzzy logic controllers
Fuzzy Sets and Systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Sum normal optimization of fuzzy membership functions
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Accuracy Improvements in Linguistic Fuzzy Modeling
Accuracy Improvements in Linguistic Fuzzy Modeling
Associations and rules in data mining: A link analysis
International Journal of Intelligent Systems - Granular Computing and Data Mining
International Journal of Intelligent Systems
Hybrid learning models to get the interpretability–accuracy trade-off in fuzzy modeling
Soft Computing - A Fusion of Foundations, Methodologies and Applications
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Constructing a user-friendly GA-based fuzzy system directly from numerical data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A 2-tuple fuzzy linguistic representation model for computing with words
IEEE Transactions on Fuzzy Systems
Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base
IEEE Transactions on Fuzzy Systems
Strategies to identify fuzzy rules directly from certainty degrees: a comparison and a proposal
IEEE Transactions on Fuzzy Systems
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
A hybrid coevolutionary algorithm for designing fuzzy classifiers
Information Sciences: an International Journal
Information Sciences: an International Journal
A new probabilistic fuzzy model: Fuzzification--Maximization (FM) approach
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Piecewise parametric polynomial fuzzy sets
International Journal of Approximate Reasoning
Improving fuzzy logic controllers obtained by experts: a case study in HVAC systems
Applied Intelligence
Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy
Evolutionary Computation
Looking for a good fuzzy system interpretability index: An experimental approach
International Journal of Approximate Reasoning
Information Sciences: an International Journal
Information Sciences: an International Journal
Fuzzy rule classifier: Capability for generalization in wood color recognition
Engineering Applications of Artificial Intelligence
International Journal of Approximate Reasoning
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Information Sciences: an International Journal
A double axis classification of interpretability measures for linguistic fuzzy rule-based systems
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Information Sciences: an International Journal
Generation of a probabilistic fuzzy rule base by learning from examples
Information Sciences: an International Journal
A genetic design of linguistic terms for fuzzy rule based classifiers
International Journal of Approximate Reasoning
Local and global optimization for Takagi-Sugeno fuzzy system by memetic genetic programming
Expert Systems with Applications: An International Journal
Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm
Computer Methods and Programs in Biomedicine
A novel fuzzy-based expert system for RET selection
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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One of the problems that focus the research in the linguistic fuzzy modeling area is the trade-off between interpretability and accuracy. To deal with this problem, different approaches can be found in the literature. Recently, a new linguistic rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the linguistic 2-tuples representation that allows the lateral displacement of a label considering an unique parameter. This way to work involves a reduction of the search space that eases the derivation of optimal models and therefore, improves the mentioned trade-off. Based on the 2-tuples rule representation, this work proposes a new method to obtain linguistic fuzzy systems by means of an evolutionary learning of the data base a priori (number of labels and lateral displacements) and a simple rule generation method to quickly learn the associated rule base. Since this rule generation method is run from each data base definition generated by the evolutionary algorithm, its selection is an important aspect. In this work, we also propose two new ad hoc data-driven rule generation methods, analyzing the influence of them and other rule generation methods in the proposed learning approach. The developed algorithms will be tested considering two different real-world problems.