Stable Factorization of 2-D Polynomials using Neural Networks
Document Type
Paper
Publication Date
12-1-1997
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.
Montclair State University Digital Commons Citation
Antoniou, George; Perantonis, Stavros J.; Ampazis, Nikolaos; and Varoufakis, Stavros J., "Stable Factorization of 2-D Polynomials using Neural Networks" (1997). Department of Computer Science Faculty Scholarship and Creative Works. 556.
https://digitalcommons.montclair.edu/compusci-facpubs/556