Genetic Algorithms For Fuzzy System Identification
Given the high degree of nonlinearity of the output of a fuzzy system, traditional linear optimization tools do have their limitations. Genetic algorithms have demonstrated to be a robust and very powerful tool to perform tasks such as the generation of fuzzy rule base, optimization of fuzzy rule bases, generation of membership functions, and tuning of membership functions (Cordón et al., 2001a). 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).
Read more about this topic: Genetic Fuzzy Systems
Famous quotes containing the words genetic, fuzzy and/or system:
“Nature, we are starting to realize, is every bit as important as nurture. Genetic influences, brain chemistry, and neurological development contribute strongly to who we are as children and what we become as adults. For example, tendencies to excessive worrying or timidity, leadership qualities, risk taking, obedience to authority, all appear to have a constitutional aspect.”
—Stanley Turecki (20th century)
“What do you think of us in fuzzy endeavor, you whose directions are sterling, whose lunge is straight?
Can you make a reason, how can you pardon us who memorize the rules and never score?”
—Gwendolyn Brooks (b. 1917)
“When the finishing stroke was put to his work, it suddenly expanded before the eyes of the astonished artist into the fairest of all the creations of Brahma. He had made a new system in making a staff, a world with full and fair proportions; in which, though the old cities and dynasties had passed away, fairer and more glorious ones had taken their places.”
—Henry David Thoreau (1817–1862)