A fuzzy control system is a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively).
Fuzzy logic is widely used in machine control. The term "fuzzy" refers to the fact that the logic involved can deal with concepts that cannot be expressed as the "true" or "false" but rather as "partially true". Although alternative approaches such as genetic algorithms and neural networks can perform just as well as fuzzy logic in many cases, fuzzy logic has the advantage that the solution to the problem can be cast in terms that human operators can understand, so that their experience can be used in the design of the controller. This makes it easier to mechanize tasks that are already successfully performed by humans.
Fuzzy logic was proposed by Lotfi A. Zadeh of the University of California at Berkeley in a 1965 paper. He elaborated on his ideas in a 1973 paper that introduced the concept of "linguistic variables", which in this article equates to a variable defined as a fuzzy set. Other research followed, with the first industrial application, a cement kiln built in Denmark, coming on line in 1975.
Fuzzy systems were initially implemented in Japan.
Interest in fuzzy systems was sparked by Seiji Yasunobu and Soji Miyamoto of Hitachi, who in 1985 provided simulations that demonstrated the feasibility of fuzzy control systems for the Sendai Subway. Their ideas were adopted, and fuzzy systems were used to control accelerating, braking, and stopping when the Namboku Line opened in 1987.
In 1987, Takeshi Yamakawa demonstrated the use of fuzzy control, through a set of simple dedicated fuzzy logic chips, in an "inverted pendulum" experiment. This is a classic control problem, in which a vehicle tries to keep a pole mounted on its top by a hinge upright by moving back and forth.
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Explore le raisonnement incertain, les réseaux bayésiens et la résolution stochastique, soulignant l'importance de la logique probabiliste et de l'enlèvement.
La logique floue (fuzzy logic, en anglais) est une logique polyvalente où les valeurs de vérité des variables — au lieu d'être vrai ou faux — sont des réels entre 0 et 1. En ce sens, elle étend la logique booléenne classique avec des . Elle consiste à tenir compte de divers facteurs numériques pour qu'on souhaite acceptable.
Un système intelligent flou (SIF) est un système qui intègre (implémente) de l’expertise humaine et qui vise à automatiser (imiter) le raisonnement d’experts humains face à des systèmes complexes. Il constitue une part importante de l’intelligence artificielle et du soft computing. Un système intelligent flou se base sur la théorie logique qu'est la logique floue.
La théorie des sous-ensembles flous est une théorie mathématique du domaine de l’algèbre abstraite. Elle a été développée par Lotfi Zadeh en 1965 afin de représenter mathématiquement l'imprécision relative à certaines classes d'objets et sert de fondement à la logique floue. Les sous-ensembles flous (ou parties floues) ont été introduits afin de modéliser la représentation humaine des connaissances, et ainsi améliorer les performances des systèmes de décision qui utilisent cette modélisation.
Research in artificial intelligence and bioinspired algorithms is still being actively pursued in different fields of engineering. In this work, Brain Emotional Learning Based Intelligent Controller (
Research in artificial intelligence and bioinspired algorithms is still being actively pursued in different fields of engineering. In this work, Brain Emotional Learning Based Intelligent Controller (
This paper proposed a fuzzy controller for the autonomous navigation problem of robotic systems in a dynamic and uncertain environment. In particular, we are interested in determining the robot motion