Table of Contents
Advances in Artificial Neural Systems
Volume 2013, Article ID 292567, 7 pages
http://dx.doi.org/10.1155/2013/292567
Research Article

An Efficient Constrained Learning Algorithm for Stable 2D IIR Filter Factorization

1Department of Financial and Management Engineering, University of the Aegean, 82100 Chios, Greece
2Institute of Informatics and Telecommunications, NCSR “Demokritos”, 15310 Athens, Greece

Received 9 November 2012; Accepted 12 January 2013

Academic Editor: Tingwen Huang

Copyright © 2013 Nicholas Ampazis and Stavros J. Perantonis. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A constrained neural network optimization algorithm is presented for factorizing simultaneously the numerator and denominator polynomials of the transfer functions of 2-D IIR filters. The method minimizes a cost function based on the frequency response of the filters, along with simultaneous satisfaction of appropriate constraints, so that factorization is facilitated and the stability of the resulting filter is respected.