Efficient fuzzy partition of pattern space for classification problems
Fuzzy Sets and Systems - Special issue on fuzzy data analysis
Hierarchical fuzzy control of multivariable systems
Fuzzy Sets and Systems
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
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
A learning process for fuzzy control rules using genetic algorithms
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Fuzzy Modeling for Control
Fuzzy Modelling: Paradigms and Practices
Fuzzy Modelling: Paradigms and Practices
Multistrategy Learning
Machine Learning
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems
Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems
Fuzzy Theory Systems: Techniques and Applications
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The Effects of Training Set Size on Decision Tree Complexity
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
On the construction of hierarchical fuzzy systems models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Interval-valued GA-P algorithms
IEEE Transactions on Evolutionary Computation
Improving the interpretability of TSK fuzzy models by combining global learning and local learning
IEEE Transactions on Fuzzy Systems
A proposal for improving the accuracy of linguistic modeling
IEEE Transactions on Fuzzy Systems
Linguistic modeling by hierarchical systems of linguistic rules
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
A linguistic truth-valued reasoning approach in decision making with incomparable information
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Fuzzy theory and technology with applications
Logic-based fuzzy networks: A study in system modeling with triangular norms and uninorms
Fuzzy Sets and Systems
Linguistic modelling based on semantic similarity relation among linguistic labels
Fuzzy Sets and Systems
A hybrid promoter analysis methodology for prokaryotic genomes
Fuzzy Sets and Systems
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Rule base simplification by using a similarity measure of fuzzy sets
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Although linguistic models are highly descriptive, they suffer from inaccuracy in some complex problems. This fact is due to problems related to the inflexibility of the linguistic rule structure that has been considered. Moreover, methods often employed to design these models from data are also biased by the former structure and by their nature, which is close to prototype identification algorithms.In order to deal with these problems of linguistic modeling, an extension of the knowledge base of linguistic fuzzy rule-based systems was previously introduced, i.e., the hierarchical knowledge base (HKB) (IEEE Trans. Fuzzy Systems 10 (1) (2002) 2). Hierarchical linguistic fuzzy models, derived from this structure, are viewed as a class of local modeling approaches. They attempt to solve a complex modeling problem by decomposing it into a number of simpler linguistically interpretable subproblems. From this perspective, linguistic modeling using an HKB can be regarded as a search for a decomposition of a non-linear system that gives a desired balance between the interpretability and the accuracy of the model. Using this approach, we are able to effectively explore the fact that the complexity of the systems is usually not uniform.We propose a well-defined hierarchical environment adopting a more general treatment than the typical prototype-oriented learning methods. This iterative hierarchical methodology takes the HKB as a base and performs a wide variety of linguistic modeling. More specifically, from fully interpretable to fully accurate, as well as intermediate trade-offs, hierarchical linguistic models.With the aim of analyzing the behavior of the proposed methodology, two real-world electrical engineering distribution problems from Spain have been selected. Successful results were obtained in comparison with other system modeling techniques.