Search results
Results from the WOW.Com Content Network
Abe Mamdani was born in Tanzania in June 1942. He was educated in India and in 1966 he went to the UK. [2] He obtained his PhD at Queen Mary College, University of London. After that he joined its Electrical Engineering Department [2] In 1975 he introduced a new method of fuzzy inference systems, which was called 'Mamdani-Type Fuzzy Inference'. [3]
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).
1974, E.H. Mamdani, Applications of fuzzy algorithms for control of simple dynamic plant, Proc. IEE 121 1584 - 1588. 1995, A. Bastian, I. Hayashi: "An Anticipating Hybrid Genetic Algorithm for Fuzzy Modeling", Journal of Japan Society for Fuzzy Theory and Systems, Vol.10, pp. 801–810
Type-2 fuzzy sets and systems generalize standard Type-1 fuzzy sets and systems so that more uncertainty can be handled. From the beginning of fuzzy sets, criticism was made about the fact that the membership function of a type-1 fuzzy set has no uncertainty associated with it, something that seems to contradict the word fuzzy, since that word has the connotation of much uncertainty.
Fuzzy logic is an important concept in medical decision making. Since medical and healthcare data can be subjective or fuzzy, applications in this domain have a great potential to benefit a lot by using fuzzy-logic-based approaches. Fuzzy logic can be used in many different aspects within the medical decision making framework.
Lotfi Aliasker Zadeh [5] (/ ˈ z ɑː d eɪ /; Azerbaijani: Lütfi Rəhim oğlu Ələsgərzadə; [6] Persian: لطفی علیعسکرزاده; [2] 4 February 1921 – 6 September 2017) [1] [3] was a mathematician, computer scientist, electrical engineer, artificial intelligence researcher, and professor [7] of computer science at the University of California, Berkeley.
Neuro-fuzzy hybridization is widely termed as fuzzy neural network (FNN) or neuro-fuzzy system (NFS) in the literature. Neuro-fuzzy system (the more popular term is used henceforth) incorporates the human-like reasoning style of fuzzy systems through the use of fuzzy sets and a linguistic model consisting of a set of IF-THEN fuzzy rules.
The place of defuzzification in a fuzzy control system A particular defuzzification method. Defuzzification is the process of producing a quantifiable result in crisp logic, given fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in fuzzy control systems.