Constrained Learning in Neural Networks: Application to Stable Factorization of 2-D Polynomials

Stavros Perantonis, Nikolaos Ampazis, Stavros Varoufakis, George Antoniou

Research output: Contribution to journalArticle

24 Scopus citations

Abstract

Adaptive artificial neural network techniques are introduced and applied to the factorization of 2-D second order polynomials. The proposed neural network is trained using a constrained learning algorithm that achieves minimization of the usual mean square error criterion along with simultaneous satisfaction of multiple equality and inequality constraints between the polynomial coefficients. Using this method, we are able to obtain good approximate solutions for non-factorable polynomials. By incorporating stability constraints into the formalism, our method can be successfully used for the realization of stable 2-D second order IIR filters in cascade form.

Original languageEnglish
Pages (from-to)5-14
Number of pages10
JournalNeural Processing Letters
Volume7
Issue number1
DOIs
StatePublished - 1 Jan 1998

Keywords

  • Constrained learning
  • Factorization
  • Feedforward networks
  • IIR filters
  • Polynomials
  • Stability

Fingerprint Dive into the research topics of 'Constrained Learning in Neural Networks: Application to Stable Factorization of 2-D Polynomials'. Together they form a unique fingerprint.

  • Cite this