Bayesian estimation of discrete-time cellular neural network coefficients

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. Th...

Improve Estimation and Operation of Optimal Power Flow(OPF) Using Bayesian Neural Network

The future of development and design is impossible without study of Power Flow(PF), exigency the system outcomes load growth, necessity add generators, transformers and power lines in  power system. The urgency for Optimal Power Flow (OPF) studies, in addition to the items listed for the PF and in order to achieve the objective functions. In this paper has been used cost of generator fuel, acti...

BAYESIAN ESTIMATION OF TIME-VARYING SYSTEMS: Discrete-Time Systems

Dynamics for Discrete-Time Cellular Neural Networks

This presentation investigates the dynamics of discrete-time cellular neural networks (DT-CNN). In contrast to classical neural networks that are mostly gradient-like systems, DT-CNN possesses both complete stability and chaotic behaviors as different parameters are considered. An energylike function which decreases along orbits of DT-CNN as well as the existence of a globally attracting set ar...

Efficient Estimation of Bayesian VARMAs with Time-Varying Coefficients

Empirical work in macroeconometrics has been mostly restricted to using VARs, even though there are strong theoretical reasons to consider general VARMAs. A number of articles in the last two decades have conjectured that this is because estimation of VARMAs is perceived to be challenging and proposed various ways to simplify it. Nevertheless, VARMAs continue to be largely dominated by VARs, pa...