Genetic fuzzy systems

Rok, strany: 1997, 93 - 121

The automatic definition of a fuzzy system can be considered in many cases as an optimization or search process. Genetic Algorithms (GAs) are the best known and most widely used global search technique with an ability to explore and exploit a given operating space using available performance measures. GAs are known to be capable of finding near optimal solutions in complex search spaces. A priori knowledge of a fuzzy system may come in the form of known linguistic variables, fuzzy membership functions parameters, fuzzy rules, number of rules, etc. The generic code structure and independent performance features of GAs make them suitable candidates for incorporating a priori knowledge. The search capabilities and ability for incorporating a priori knowledge have extended the use of GAs in the development of a wide range of methods for designing fuzzy systems over the last few years. Systems applying these design approaches have received the general name of Genetic Fuzzy Systems (GFSs). In this tutorial we summarize different GFSs approaches, focusing our presentation on genetic fuzzy rule-based systems.

ISO 690:

Herrera, F., Magdalena, L. 1997. Genetic fuzzy systems. In Tatra Mountains Mathematical Publications, vol. 13, no.4, pp. 93-121. 1210-3195.

APA:

Herrera, F., Magdalena, L. (1997). Genetic fuzzy systems. Tatra Mountains Mathematical Publications, 13(4), 93-121. 1210-3195.