Learning of Type-2 Fuzzy Logic Systems using Simulated Annealing.

De Montfort University Open Research Archive

Show simple item record

dc.contributor.author Almaraashi, Majid
dc.date.accessioned 2012-10-30T15:27:28Z
dc.date.available 2012-10-30T15:27:28Z
dc.date.issued 2012
dc.identifier.uri http://hdl.handle.net/2086/7670
dc.description.abstract This thesis reports the work of using 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 type-1 and type-2 fuzzy logic systems to maximise their modelling ability. Therefore, it presents the combination of simulated annealing with three models, type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and general type-2 fuzzy logic systems to model four bench-mark problems including real-world problems. These problems are: noise-free Mackey-Glass time series forecasting, noisy Mackey-Glass time series forecasting and two real world problems which are: the estimation of the low voltage electrical line length in rural towns and the estimation of the medium voltage electrical line maintenance cost. The type-1 and type-2 fuzzy logic systems models are compared in their abilities to model uncertainties associated with these problems. Also, issues related to this combination between simulated annealing and fuzzy logic systems including type-2 fuzzy logic systems are discussed. The thesis contributes to knowledge by presenting novel contributions. The first is a novel approach to design interval type-2 fuzzy logic systems using the simulated annealing algorithm. Another novelty is related to the first automatic design of general type-2 fuzzy logic system using the vertical slice representation and a novel method to overcome some parametrisation difficulties when learning general type-2 fuzzy logic systems. The work shows that interval type-2 fuzzy logic systems added more abilities to modelling information and handling uncertainties than type-1 fuzzy logic systems but with a cost of more computations and time. For general type-2 fuzzy logic systems, the clear conclusion that learning the third dimension can add more abilities to modelling is an important advance in type-2 fuzzy logic systems research and should open the doors for more promising research and practical works on using general type-2 fuzzy logic systems to modelling applications despite the more computations associated with it. en
dc.language.iso en en
dc.publisher De Montfort University en
dc.subject Type-2 Fuzzy Logic Systems en
dc.subject Simulated Annealing en
dc.title Learning of Type-2 Fuzzy Logic Systems using Simulated Annealing. en
dc.type Thesis or dissertation en
dc.publisher.department Faculty of Technology en
dc.publisher.department Centre for Computational Intelligence en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD en

Files in this item

This item appears in the following Collection(s)

Show simple item record