Structure identification of fuzzy model
Fuzzy Sets and Systems
Applied multivariate statistical analysis
Applied multivariate statistical analysis
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to fuzzy control
An introduction to fuzzy control
Combination of rules or their consequences in fuzzy expert systems
Fuzzy Sets and Systems - Special issue on expert decision support systems
Sugeno model, fuzzy discretization, and the EM algorithm
Fuzzy Sets and Systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Proceedings of the 32nd conference on Winter simulation
Fuzzy Control: Synthesis and Analysis
Fuzzy Control: Synthesis and Analysis
Differences between t-norms in fuzzy control
International Journal of Intelligent Systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper, we propose an indirect method to fuzzy modeling which implements a clustering algorithm to build a linguistic fuzzy controller. Based on output data clustering and projection onto the input spaces, the number of clusters is determined and rules are generated automatically. A new methodology based on output sensitivity is developed for input variable selection. Then, implementing an Adapted Neural Network for the selection of membership functions optimizes all membership function parameters. The unbounded parameters of fuzzy operators and the inference methods of FATI (First Aggregate, Then Infer) and FITA (First Infer, Then Aggregate) are optimized through a simple and efficient tuning strategy.