Fuzzy logic system applied to classification problems in railways

Abstract

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.