Bayesian estimation of discrete-time cellular neural network coefficients

Authors

HAKAN METİN ÖZER
ATİLLA ÖZMEN
HABİB ŞENOL

DOI

10.3906/elk-1510-87

Abstract

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 Metropolis-Hastings algorithm where the proposal distribution function is symmetric, and resulting samples are then averaged to find the minimum mean square error (MMSE) estimate of the network coefficients. A couple of image processing applications are performed using these estimated parameters and the results are compared with those of some well-known methods.

Keywords

Bayesian~learning, cellular neural networks, Metropolis-Hastings, estimation

First Page

2363

Last Page

2374

Recommended Citation

ÖZER, HAKAN METİN; ÖZMEN, ATİLLA; and ŞENOL, HABİB (2017) "Bayesian estimation of discrete-time cellular neural network coefficients," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 25: No. 3, Article 59. https://doi.org/10.3906/elk-1510-87
Available at: https://journals.tubitak.gov.tr/elektrik/vol25/iss3/59