Now showing items 1-2 of 2

  • Bayesian estimation of discrete-time cellular neural network coefficients 

    A new method for finding the network coefficients of a discrete-time cellular neural network (DTCNN) is proposed. This new method uses a probabilistic approach that itself uses Bayesian learning to estimate the network coefficients. A posterior probability density function (PDF) is composed using the likelihood and prior PDFs derived from the system model and prior information respectively. This posterior PDF is used to draw samples with the help of the Metropolis algorithm a special case of the ...

  • Bayesian learning for cellular neural networks 

    Authors:Ozer, Metin
    Publisher and Date:(Kadir Has University, 2013)
    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 ...