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Interval type-2 fuzzy logic systems: theory and design

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dc.contributor Ph.D. Program in Electrical and Electronic Engineering.
dc.contributor.advisor Kaynak, Okyay,
dc.contributor.author Kayacan, Erdal.
dc.date.accessioned 2023-03-16T10:25:04Z
dc.date.available 2023-03-16T10:25:04Z
dc.date.issued 2011.
dc.identifier.other EE 2011 K381 PhD
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/13102
dc.description.abstract This Ph.D. dissertation has four main objectives. Firstly, the noise reduction property of type-2 fuzzy logic systems that use a novel type-2 fuzzy membership function is studied. A number of papers exist in literature that claim the performance of type-2 fuzzy logic systems is better than that of type-1 fuzzy logic systems under noisy conditions, and this claim is supported by simulation studies only for some specific systems. In this dissertation, a simpler type-2 fuzzy logic system is considered with the novel membership function proposed in which the effect of input noise in the rule base is shown numerically in a general way. Secondly, fuzzy c-means clustering algorithm is proposed for type-2 fuzzy logic systems to determine the initial places of the membership functions to ensure that the gradient descent algorithm used afterwards converges in a shorter time. Thirdly, Levenberg-Marquardt algorithm is proposed for type-2 fuzzy neural networks. While conventional gradient descent algorithms use only the first order derivative, the proposed algorithm used in this dissertation benefits from the first and the second-order derivatives which makes the training procedure faster. Finally, a novel sliding mode control theory-based learning algorithm is proposed to train the parameters of the type-2 fuzzy neural networks. In the approach, instead of trying to minimize an error function, the weights of the network are tuned by the proposed algorithm in a way that the error is enforced to satisfy a stable equation. The parameter update rules are derived for both Gaussian and triangular type-2 fuzzy membership functions, and the convergence of the weights is proven by Lyapunov stability method. The simulation results indicate that the type-2 fuzzy structure with the proposed learning algorithm results in a better performance than its type-1 fuzzy counterpart.
dc.format.extent 30cm.
dc.publisher Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2011.
dc.relation Includes appendices.
dc.relation Includes appendices.
dc.subject.lcsh Fuzzy systems.
dc.subject.lcsh Fuzzy logic.
dc.subject.lcsh Logic design -- Computer programs.
dc.title Interval type-2 fuzzy logic systems: theory and design
dc.format.pages xix, 130 leaves ;


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