A hierarchical fuzzy system with high input dimensions for forecasting foreign exchange rates
Christopher Cheong · International Journal of Artificial Intelligence and Soft Computing · 2008
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.