A new partitioning neural network model for recursively finding arbitrary roots of higher order arbitrary polynomials
type: Item
instance of: scholarly article
title: A new partitioning neural network model for recursively finding arbitrary roots of higher order arbitrary polynomials
zbMATH Open document ID: 1063.65038
author: De-Shuang Huang
author: H. S. Ip Horace
author: C. K. Law Ken
author: Zheru Chi
author: Hau-San Wong
published in: Applied Mathematics and Computation
publication date: 2005-02-23T00:00:00Z
Mathematics Subject Classification ID: 65H05
Mathematics Subject Classification ID: 12Y05
Mathematics Subject Classification ID: 26C10
Mathematics Subject Classification ID: 30C15
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 2137744
zbMATH Keywords: Feedforward neural network
zbMATH Keywords: Roots-finder
zbMATH Keywords: Polynomial
zbMATH Keywords: Complex constrained learning algorithm
zbMATH Keywords: Partitioning
zbMATH Keywords: Laguerre method
zbMATH Keywords: Muller method
zbMATH Keywords: Jenkins-Traub method
zbMATH Keywords: Numerical examples
MaRDI profile type: Publication
full work available at URL: j.amc.2004.03.028
OpenAlex ID: W2027528539
cites work: Q5289009
cites work: Q4195836
cites work: Phase unwrapping by factorization
cites work: Dilation method for finding close roots of polynomials based on constrained learning neural networks
cites work: Learning representations by back-propagating errors
DOI: 10.1016/J.AMC.2004.03.028
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description: scientific article; zbMATH DE number 2137744