Fuzzy modelling through logic optimization
International Journal of Approximate Reasoning
Collaborative clustering with the use of Fuzzy C-Means and its quantification
Fuzzy Sets and Systems
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
International Journal of Approximate Reasoning
Fuzzy qualitative trigonometry
International Journal of Approximate Reasoning
A selection scheme for excluding defective rules of evolutionary fuzzy path planning
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Decision making with imprecise parameters
International Journal of Approximate Reasoning
T-S fuzzy modeling based on support vector learning
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Evolutionary computation and its applications in neural and fuzzy systems
Applied Computational Intelligence and Soft Computing
Behavioral analysis in social networks: an approach based on intelligent system
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Computers in Biology and Medicine
Process control using genetic algorithm and ant colony optimization algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This study is concerned with a general methodology of identification of fuzzy models. Unlike numeric models, fuzzy models operate at a level of information granules - fuzzy sets - and this aspect brings up an important design requirement of transparency of the model. We propose a three-phase development framework by distinguishing between structural and parametric optimization processes. The underlying topology of the model dwells on fuzzy neural networks - architectures governed by fuzzy logic and equipped with parametric flexibility. Two general optimization mechanisms are explored: the structural optimization is realized via genetic programming whereas for the ensuing detailed parametric optimization we proceed with gradient-based learning. The main advantages of this approach are discussed in detail. The study is illustrated with the aid of a numeric example that provides a detailed insight into the performance of the fuzzy models and quantifies crucial design issues.