HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Expert Systems with Applications: An International Journal
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Information Sciences: an International Journal
Learning flexible structured linguistic fuzzy rules for Mamdani fuzzy systems
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Optimization of shared autonomy vehicle control architectures for swarm operations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Fuzzy model tuning using simulated annealing
Expert Systems with Applications: An International Journal
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There are two tasks in the design of linguistic fuzzy models for a concrete application: The derivation of the linguistic rule base and the setup of the inference system and the defuzzification method. Traditionally, the derivation of the linguistic rule base has been considered the most important task, but the use of the appropriate aggregation connectors in the inference system and the defuzzification interface can improve the fuzzy system behavior. In this paper, we take in consideration this idea, we propose an evolutionary learning method to learn a linguistic rule base and the parametric aggregation connectors of the inference and defuzzification in a single step. The aim of this methodology is to make possible a high level of positive synergy between the linguistic rule base and the aggregation connectors, improving the accuracy of the linguistic Mamdani fuzzy systems. Our proposal has shown good results solving three different applications. We introduce a statistical analysis of results for validating the model behavior on the applications used in the experimental study. We must remark that we present an experimental study with a double intention: (a) to compare the behavior of the new approach in comparison with those ones that first learn the rule base and then adapt the connectors, and (b) to analyze the rule bases obtained with fixed aggregation connectors and with the adaptive ones for showing the changes on the consequent rules, changes on labels that produce a better behavior of the linguistic model than the classic ones.