Genetic fuzzy systems

When it comes to automatically identifying and building a fuzzy system, given the high degree of nonlinearity of the output, traditional linear optimization tools have several limitations.

Fuzzy systems are fundamental methodologies to represent and process linguistic information, with mechanisms to deal with uncertainty and imprecision.

With such remarkable attributes, fuzzy systems have been widely and successfully applied to control, classification and modeling problems (Mamdani, 1974) (Klir and Yuan, 1995) (Pedrycz and Gomide, 1998).

Much work has been done to develop or adapt methodologies that are capable of automatically identifying a fuzzy system from numerical data.

All these tasks can be considered as optimization or search processes within large solution spaces (Bastian and Hayashi, 1995) (Yuan and Zhuang, 1996) (Cordón et al., 2001b).