TY - JOUR
T1 - Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice
AU - Almaraashi, Majid
AU - John, Robert
AU - Hopgood, Adrian
AU - Ahmadi, Samad
PY - 2016/9/10
Y1 - 2016/9/10
N2 - 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.
AB - 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.
KW - simulated annealing
KW - Interval type-2 fuzzy logic systems
KW - general type-2 fuzzy logic systems
KW - learning
UR - http://www.scopus.com/inward/record.url?scp=84965180106&partnerID=8YFLogxK
UR - http://www.sciencedirect.com/science/article/pii/S0020025516302225/pdfft?md5=25b1660a344fd85a5703260057e01065&pid=1-s2.0-S0020025516302225-main.pdf
UR - https://www.dora.dmu.ac.uk/bitstream/handle/2086/12236/SA-T2FLS_REVIEW.pdf?sequence=1&isAllowed=y
U2 - 10.1016/j.ins.2016.03.047
DO - 10.1016/j.ins.2016.03.047
M3 - Article
AN - SCOPUS:84965180106
SN - 0020-0255
VL - 360
SP - 21
EP - 42
JO - Information Sciences
JF - Information Sciences
ER -