Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Evolving fuzzy rule based controllers using genetic algorithms
Fuzzy Sets and Systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A GA paradigm for learning fuzzy rules
Fuzzy Sets and Systems - Special issue on connectionist and hybrid connectionist systems for approximate reasoning
Data mining methods for knowledge discovery
Data mining methods for knowledge discovery
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Semantic constraints for membership function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A self-learning fuzzy logic controller using genetic algorithms with reinforcements
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
International Journal of Hybrid Intelligent Systems
Expert Systems with Applications: An International Journal
Integrated Computer-Aided Engineering
International Journal of Knowledge-based and Intelligent Engineering Systems
Engineering Applications of Artificial Intelligence
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
A modified gradient-based neuro-fuzzy learning algorithm and its convergence
Information Sciences: an International Journal
Breast-Cancer identification using HMM-fuzzy approach
Computers in Biology and Medicine
An evolution of geometric structures algorithm for the automatic classification of HRR radar targets
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Fuzzy transforms method in prediction data analysis
Fuzzy Sets and Systems
Generation of a probabilistic fuzzy rule base by learning from examples
Information Sciences: an International Journal
A hybrid fuzzy rule-based multi-criteria framework for sustainable project portfolio selection
Information Sciences: an International Journal
A hierarchical approach to multi-class fuzzy classifiers
Expert Systems with Applications: An International Journal
Adaptive fuzzy clustering based anomaly data detection in energy system of steel industry
Information Sciences: an International Journal
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A methodology for the encoding of the chromosome of a genetic algorithm (GA) is described in the paper. The encoding procedure is applied to the problem of automatically generating fuzzy rule-based models from data. Models generated by this approach have much of the flexibility of black-box methods, such as neural networks. In addition, they implicitly express information about the process being modelled through the linguistic terms associated with the rules. They can be applied to problems that are too complex to model in a first principles sense and can reduce the computational overhead when compared to established first principles based models. The encoding mechanism allows the rule base structure and parameters of the fuzzy model to be estimated simultaneously from data. The principle advantage is the preservation of the linguistic concept without the need to consider the entire rule base. The GA searches for the optimum solution given a comparatively small number of rules compared to all possible. This minimises the computational demand of the model generation and allows problems with realistic dimensions to be considered. A further feature is that the rules are extracted from the data without the need to establish any information about the model structure a priori. The implementation of the algorithm is described and the approach is applied to the modelling of components of heating ventilating and air-conditioning systems.