Browsing by Author "Ozer, Metin"

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    Master Thesis
    Bayesian Learning for Cellular Neural Networks
    (Kadir Has Üniversitesi, 2013) Ozer, Metin; Ozmen, Atilla
    Cellular Neural Networks have been an active research eld since their introduction in the late 80s. Several training algorithms are proposed since then. All have their advantages and disadvantages. Most of them uses deterministic methods to acquire the network parameters. in this thesis a new training method is proposed for Cellular Neural Networks and Discrete-Time Cellular Neural Networks are used for implemented applications. This new method is a probabilistic method. Maximum A Posteriori estimation is used to estimate the network parameters thus making this method a Bayesian learning method. A Cellular Neural Network is nonlinear in the sense of its activation function. For the same reason modeling of a Cellular Neural Network is also nonlinear. Using Maximum A Posteriori estimation on a nonlinear system causes some problems. To cope with this problems in the estimation process of network parameters Metropolis-Hastings algorithm which is one of Monte Carlo Markov Chain methods is used for generating the samples needed from the resulting distribution. After the network is trained it is tested against known algorithms to verify the training process. Discrete-Time Cellular Neural Networks are mostly used for image processing applications. Many dierent kind of applications can be applied using dierent network parameters without changing the cellular network architecture. A couple of applications are picked from this pool and using the estimated parameters Cellular Neural Networks are used to perform some image processing algorithms. This operations are performed by computer models and simulations. -- Abstract'tan.