A fuzzy logic based efficient energy saving approach for domestic heating systems
Integrated Computer-Aided Engineering
A case study for learning behaviors in mobile robotics by evolutionary fuzzy systems
Expert Systems with Applications: An International Journal
Comparative Analysis of Evolutionary Fuzzy Models for Premises Valuation Using KEEL
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Hierarchical cluster-based multispecies particle-swarm optimization for fuzzy-system optimization
IEEE Transactions on Fuzzy Systems
Machine scheduling in custom furniture industry through neuro-evolutionary hybridization
Applied Soft Computing
Evolutionarily adjusting membership functions in Takagi-Sugeno fuzzy systems
International Journal of Intelligent Information and Database Systems
Financial time series forecasting with a bio-inspired fuzzy model
Expert Systems with Applications: An International Journal
A hybrid fuzzy intelligent agent-based system for stock price prediction
International Journal of Intelligent Systems
Differential evolution with local information for neuro-fuzzy systems optimisation
Knowledge-Based Systems
On the use of meta-learning for instance selection: An architecture and an experimental study
Information Sciences: an International Journal
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This work presents the use of local fuzzy prototypes as a new idea to obtain accurate local semantics-based Takagi–Sugeno–Kang (TSK) rules. This allow us to start from prototypes considering the interaction between input and output variables and taking into account the fuzzy nature of the TSK rules. To do so, a two-stage evolutionary algorithm based on MOGUL (a methodology to obtain Genetic Fuzzy Rule-Based Systems under the Iterative Rule Learning approach) has been developed to consider the interaction between input and output variables. The first stage performs a local identification of prototypes to obtain a set of initial local semantics-based TSK rules, following the Iterative Rule Learning approach and based on an evolutionary generation process within MOGUL (taking as a base some initial linguistic fuzzy partitions). Because this generation method induces competition among the fuzzy rules, a postprocessing stage to improve the global system performance is needed. Two different processes are considered at this stage, a genetic niching-based selection process to remove redundant rules and a genetic tuning process to refine the fuzzy model parameters. The proposal has been tested with two real-world problems, achieving good results. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 909–941, 2007.