Abstract:
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 the modeling of four benchmark
problems including real-world problems. The type-2 fuzzy logic system models
are compared in their ability to model uncertainties associated with these problems.
Issues related to this combination between simulated annealing and fuzzy logic systems,
including type-2 fuzzy logic systems, are discussed. The results demonstrate that
learning the third dimension in type-2 fuzzy sets with a deterministic defuzzifier can
add more capability to modeling than interval type-2 fuzzy logic systems. This finding
can be seen as an important advance in type-2 fuzzy logic systems research and should
increase the level of interest in the modeling applications of general type-2 fuzzy logic
systems, despite their greater computational load.
