This paper reports the use of simulated annealing to design more efficient fuzzy logic systems to model problems with associated uncertainties. Simulated annealing is used within this work as a method for learning the best configurations of interval and general type-2 fuzzy logic systems to maximize their modeling ability. The combination of simulated annealing with these models is presented in...

Interval Type-2 Fuzzy Logic Controller (IT2FLC) where the fuzzy parameters, e.g. Fuzzy Membership Functions and Fuzzy Rule Bases are tuned by Genetic Algorithm (GAs) known as Genetic Interval Type-2 Fuzzy System (GIT2FS). The GFS are discussed briefly, later, the proposed design is explained. Key wordsGA, Interval Type-2 Fuzzy Logic Controller (IT2FLC), Genetic Interval Type2 Fuzzy System (GIT2...

Type-2 fuzzy logic to make up for the lack of a type of fuzzy logic in dealing with uncertainty, object contains uncertainty is strong; the application of type-2 fuzzy logic advantage is more obvious. In this paper, type-2s of fuzzy logic for power load time series forecasting, good results were obtained. According to the power load has strong randomness it is difficult to accurately forecast p...

Fuzzy Logic, introduced by Zadeh along with his introduction of fuzzy sets, is a continuous multi-valued logic system. Hence, it is a generalization of the classical logic and the classical discrete multi-valued logic (e.g. Łukasiewicz’ three/many-valued logic). Throughout the years Zadeh and other researches have introduced extensions to the theory of fuzzy setts and fuzzy logic. Notable exten...

Fuzzy Logic Systems are widely recognized to be successful at modelling uncertainty in a large variety of applications. While recently interval type-2 fuzzy logic has been credited for the ability to better deal with large amounts of uncertainty, general type2 fuzzy logic has been a steadily growing research area. All fuzzy logic systems require the accurate specification of the membership func...

fuzzy logic, 67algebraic semantics, 53approximate reasoning, 6axiomatic extensions of MTL, 56 Bellman, R., 21bipolar possibilistic logic, 93BL logic, 51 canonical chain, 66canonical completeness, 66canonical extension, 83Carnap, R., 3comparative truth, 63compositionality, 29computational complexity, 73core fuzzy logic, 58 De Finetti B., 3degre...

There are a growing number of aerospace applications demonstrating the effectiveness of emulating human decision making using fuzzy logic. In this effort, a previously created MATLAB simulation of the classic arcade game PONG is utilized in which a fuzzy logic system that uses real-time fuzzy reasoning and awareness to represent a two-player team is able to play against various opponents. The o...