Now showing items 111-120 of 128
Interval-valued fuzzy decision trees.
Learning of Interval and General Type-2 Fuzzy Logic Systems using Simulated Annealing: Theory and Practice
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 ...
Sales intelligence using web mining.
(Springer Berlin, 2009)
This paper presents a knowledge extraction system for providing sales intelligence based on information downloaded from the WWW. The information is first located and downloaded from relevant companies’ websites and then ...
Interval type-2 fuzzy decision making
This paper concerns itself with decision making under uncertainty and the consideration of risk. Type-1 fuzzy logic by its (essentially) crisp nature is limited in modelling decision making as there is no uncertainty in ...
Type-2 fuzzy logic and the modelling of uncertainty
Type-2 Fuzzy Elliptic Membership Functions for Modeling Uncertainty
Whereas type-1 and type-2 membership functions (MFs) are the core of any fuzzy logic system, there are no performance criteria available to evaluate the goodness or correctness of the fuzzy MFs. In this paper, we make ...
Type-2 Fuzzy Alpha-Cuts
Type-2 fuzzy logic systems make use of type-2 fuzzy sets. To be able to deliver useful type-2 fuzzy logic applications we need to be able to perform meaningful operations on these sets. These operations should also ...