Uncertainty modeling of improved fuzzy functions with evolutionary systems.
This paper introduce a type-2 fuzzy function system for uncertainty modeling using evolutionary algorithms (ET2FF). The type-1 fuzzy inference systems (FISs) with fuzzy functions, which do not entail if ... then rule bases, have demonstrated better performance compared to traditional FIS. Nonetheless, the performance of these approaches is usually affected by their uncertain parameters. The proposed method implements a three-phase learning strategy to capture the uncertainties in fuzzy function systems induced by learning parameters, as well as fuzzy function structures. The improved fuzzy clustering initially finds hidden structures, and the genetic learning algorithm optimizes interval type-2 fuzzy sets to capture their optimum uncertainty interval. The proposed ET2FF architecture is evaluated using an extensive suite of real-life applications such as manufacturing process and financial market modeling. The results show that the proposed ET2FF method is comparable--if not superior--to earlier FIS in terms of generalization performance and robustness.
Celikyilmaz
Asli
A
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720-1776, USA. asli@eecs.berkeley.edu
Turksen
I Burhan
IB
D000465:Algorithms; D005075:Biological Evolution; D003198:Computer Simulation; D017143:Fuzzy Logic; D015233:Models, Statistical