Finding roots of arbitrary high order polynomials based on neural network recursive partitioning method

Deshuang Huang, Zheru Chi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)

Abstract

This paper proposes a novel recursive partitioning method based on constrained learning neural networks to find an arbitrary number (less than the order of the polynomial) of (real or complex) roots of arbitrary polynomials. Moreover, this paper also gives a BP network constrained learning algorithm (CLA) used in root-finders based on the constrained relations between the roots and the coefficients of polynomials. At the same time, an adaptive selection method for the parameter δP with the CLA is also given. The experimental results demonstrate that this method can more rapidly and effectively obtain the roots of arbitrary high order polynomials with higher precision than traditional root-finding approaches.
Original languageEnglish
Pages (from-to)232-245
Number of pages14
JournalScience in China, Series F: Information Sciences
Volume47
Issue number2
DOIs
Publication statusPublished - 1 Apr 2004

Keywords

  • Adaptive parameter selection
  • BP neural networks
  • Constrained learning algorithm
  • High order arbitrary polynomials
  • Jenkins-Traub method
  • Laguerre method
  • Muller method
  • Real or complex roots
  • Recursive partitioning method

ASJC Scopus subject areas

  • Computer Science(all)

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