About the Book
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 60. Chapters: Adaptive neuro fuzzy inference system, Bate's chip, BL (logic), Combs method, Construction of t-norms, Defuzzification, Degree of truth, European Society for Fuzzy Logic and Technology, Fuzzy architectural spatial analysis, Fuzzy associative matrix, Fuzzy associative memory, Fuzzy classification, Fuzzy cognitive map, Fuzzy Control Language, Fuzzy control system, Fuzzy electronics, Fuzzy finite element, Fuzzy markup language, Fuzzy mathematics, Fuzzy measure theory, Fuzzy number, Fuzzy pay-off method for real option valuation, Fuzzy routing, Fuzzy rule, Fuzzy set, Fuzzy Sets and Systems, Fuzzy set operations, Fuzzy subalgebra, Fuzzy transportation, High Performance Fuzzy Computing, Linear partial information, Membership function (mathematics), Monoidal t-norm logic, MV-algebra, Noise-based logic, Ordered weighted averaging aggregation operator, Perceptual computing, Possibility theory, Predicate (mathematical logic), Residuated Boolean algebra, Residuated lattice, Rough fuzzy hybridization, Sugeno integral, T-norm fuzzy logics, Type-1 OWA operators, Type-2 fuzzy sets and systems, Uncertainty theory, Vagueness, ukasiewicz logic. Excerpt: 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 itself inspires a certain skepticism, sounding equivalent to "half-baked logic" or "bogus logic," but the "fuzzy" part does not refer to a lack of rigour in the method, rather to the fact that the logic involved can deal with concepts that cannot be expressed as "true" or "false" but rather as "partially true." Although 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 first 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 largely ignored in the U.S. because they were associated with artificial intelligence, a field that periodically oversells itself, especially in the mid-1980s, resulting in a lack of credibility within the commercial domain. The Japanese did not have this prejudice. Interest in fuzzy systems was sparked by Seiji Yasunobu and Soji Miyamoto of Hitachi, who in 1985 provided simulations that demonstrated the superiority of fuzzy control systems for the Sendai railway. Their ideas were adopted, and fuzzy sys
