Stable factorization of 2-D polynomials using neural networks

G. Antoniou, S. J. Perantonis, N. Ampazis, S. J. Varoufakis

Research output: Contribution to conferencePaper

Abstract

A method is presented for the factorization of 2-D 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 non-factorable 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.

Original languageEnglish
Pages983-986
Number of pages4
StatePublished - 1 Dec 1997
EventProceedings of the 1997 13th International Conference on Digital Signal Processing, DSP. Part 2 (of 2) - Santorini, Greece
Duration: 2 Jul 19974 Jul 1997

Other

OtherProceedings of the 1997 13th International Conference on Digital Signal Processing, DSP. Part 2 (of 2)
CitySantorini, Greece
Period2/07/974/07/97

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    Antoniou, G., Perantonis, S. J., Ampazis, N., & Varoufakis, S. J. (1997). Stable factorization of 2-D polynomials using neural networks. 983-986. Paper presented at Proceedings of the 1997 13th International Conference on Digital Signal Processing, DSP. Part 2 (of 2), Santorini, Greece, .