Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks in designing fuzzy systems for real world applications
Fuzzy Sets and Systems
Constraining the optimization of a fuzzy logic controller using anenhanced genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Combinatorial rule explosion eliminated by a fuzzy rule configuration
IEEE Transactions on Fuzzy Systems
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IEEE Transactions on Fuzzy Systems
Avoiding exponential parameter growth in fuzzy systems
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
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Fuzzy systems suffer from the curse of dimensionality as the number of rules increases exponentially with the number of input dimensions. Although several methods have been proposed for eliminating the combinatorial rule explosion, none of them is fully satisfactory. In this paper, we describe a method for building fuzzy systems with high input dimensions based on the hierarchical architecture and the MacVicar-Whelan meta-rules. We tested the method by building fuzzy systems for two different applications, namely the forecasting of the Mexican and Argentinan pesos exchange rates. In both cases, our approach was successful as both fuzzy systems performed very well.