Fuzzy model identification: selected approaches
Fuzzy model identification: selected approaches
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Combining GP operators with SA search to evolve fuzzy rule based classifiers
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Journal of Global Optimization
Extracting Interpretable Fuzzy Rules from RBF Networks
Neural Processing Letters
Accuracy Improvements in Linguistic Fuzzy Modeling
Accuracy Improvements in Linguistic Fuzzy Modeling
Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining (Advanced Information Processing)
Agent-based evolutionary approach for interpretable rule-based knowledge extraction
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Simplification of fuzzy-neural systems using similarity analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybridization of fuzzy GBML approaches for pattern classification problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
Compact and transparent fuzzy models and classifiers through iterative complexity reduction
IEEE Transactions on Fuzzy Systems
Designing evolving user profile in e-CRM with dynamic clustering of Web documents
Data & Knowledge Engineering
A fuzzy modeling method via Enhanced Objective Cluster Analysis for designing TSK model
Expert Systems with Applications: An International Journal
Logic-based fuzzy networks: A study in system modeling with triangular norms and uninorms
Fuzzy Sets and Systems
IEEE Transactions on Fuzzy Systems
A genetic reduction of feature space in the design of fuzzy models
Applied Soft Computing
Granular fuzzy models: a study in knowledge management in fuzzy modeling
International Journal of Approximate Reasoning
Process control using genetic algorithm and ant colony optimization algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper a new technique for eliciting a fuzzy inference system (FIS) from data for nonlinear systems is proposed. The strategy is conducted in two phases: in the first one, subtractive clustering is applied in order to extract a set of fuzzy rules, in the second phase, the generated fuzzy rule base is refined and redundant rules are removed on the basis of an interpretability measure. Finally the centers and widths of the Membership Functions (MFs) are tuned by means differential evolution. Case studies are presented to illustrate the efficiency and accuracy of the proposed approach. The results obtained are compared and contrasted with those obtained from a conventionally neuro-fuzzy technique and the superiority of the proposed approach is highlighted.