Stable factorization of 2-D polynomials using neural networks

Author

Antoniou, Grigoris ; Perantonis, S.J. ; Ampazis, N. ; Varoufakis, S.J.

Author_Institution

Dept. of Math. & Comput. Sci., Montclair State Univ., NJ, USA

Volume

2

fYear

1997

fDate

2-4 Jul 1997

Firstpage

983

Abstract

A method is presented for the factorization of 2D second order polynomials, based on the application of artificial neural networks trained by constrained learning techniques. The approach achieves minimization of the usual mean square error criterion along with simultaneous satisfaction of constraints between the polynomial coefficients. Using this method, we are able to obtain the exact solution for factorable polynomials and good approximate solutions for nonfactorable polynomials. By incorporating additional constraints for stability into the formalism our method can be successfully used for the realization of stable IIR filters in cascade form

Keywords

IIR filters; cascade systems; filtering theory; learning (artificial intelligence); least mean squares methods; neural nets; polynomials; signal processing; 2D second-order polynomial factorization; cascade form filters; constrained learning techniques; mean square error criterion minimization; multidimensional signal processing; neural networks; polynomial coefficient constraints; stability constraints; stable IIR filter realization; stable factorization; Computer science; Costs; IIR filters; Mean square error methods; Neural networks; Polynomials;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing Proceedings, 1997. DSP 97., 1997 13th International Conference on

Conference_Location

Santorini

Print_ISBN

0-7803-4137-6

Type

conf

DOI

10.1109/ICDSP.1997.628528

Filename

628528