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.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-03-10T12:31:18Z No. of bitstreams: 1 eduardopestanadeaguiar.pdf: 7884545 bytes, checksum: 182caace21281f7afce6554505811116 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-03-13T17:18:31Z (GMT) No. of bitstreams: 1 eduardopestanadeaguiar.pdf: 7884545 bytes, checksum: 182caace21281f7afce6554505811116 (MD5) / Made available in DSpace on 2017-03-13T17:18:31Z (GMT). No. of bitstreams: 1 eduardopestanadeaguiar.pdf: 7884545 bytes, checksum: 182caace21281f7afce6554505811116 (MD5) Previous issue date: 2016-09-26 / - / This thesis presents new fuzzy models applied to classification problems. With this regards, we introduce the use of set-membership concept, derived from the adaptive filter theory, into the training procedure of type-1 and singleton/non-singleton fuzzy logic systems, in order to reduce computational complexity and to increase convergence speed. Also, we present different criteria for using together with set-membership. Furthermore, we discuss the usefulness of delta rule delta, local Lipschitz estimation, variable step size and variable step size adaptive algorithms to yield additional improvement in terms of computational complexity reduction and convergence speed. Another important contribution of this thesis is to address the height type-reduction and to propose a modified version of interval singleton type-2 fuzzy logic system, so−called upper and lower singleton type-2 fuzzy logic system. The obtained results are compared with other models reported in the literature, demonstrating the effectiveness of the proposed classifiers and revealing that the proposals are able to properly handle with uncertainties associated with the measurements and with the data that are used to tune the parameters of the model. Based on data set provided by a Brazilian railway company, the models outlined above are applied in the classification of three possible faults and the normal condition of the switch machine, which is an equipment used for handling railroad switches. Finally, this thesis discusses the use of set-membership concept into the training procedure of an interval and singleton type-2 fuzzy logic system and of an upper and lower singleton type-2 fuzzy logic system, aiming to reduce computational complexity and to increase the convergence speed and the classification ratio. Also, we discuss the adoption of different criteria together with set-membership based-techniques. The performance is based on the data set composed of images provided by the same Brazilian railway company, which covers the four possible rail head defects and the normal condition of the rail head. The reported results show that the proposed models result in improved convergence speed, slightly higher classification ratio and remarkable computation complexity reduction when we limit the number of epochs for training, which may be required due to real time constraint or low computational resource availability.