Browsing by Author "Kayacan, Erdal."

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    Grey prediction based control of a non-linear liquid level system using PID type fuzzy controller
    (Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2006., 2006.) Kayacan, Erdal.; Kaynak, Okyay,
    This thesis proposes a grey system theory based fuzzy PID controller that has a prediction capability. Although fuzzy control theory and grey system theory have completely different mathematical basics, both deal with uncertain information. In the thesis, a short description of both are given and their performance are compared on a non-linear liquid level control system. The grey model developed is examined under several different conditions and it is shown that the proposed grey fuzzy PID controller has better self-adapting characteristics. The simulation results indicate that the proposed controller has the ability to control the non-linear system accurately with a little amount of overshoot and with no steady-state error. It has, in these respects, better performance than the conventional controllers. The thesis is also intended to serve as a first reading on grey system theory and grey prediction based controllers. The fundamental concepts and mathematical basics of grey system theory are therefore explained in simple terms.
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    Interval type-2 fuzzy logic systems: theory and design
    (Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2011., 2011.) Kayacan, Erdal.; Kaynak, Okyay,
    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.