A new partitioning neural network model for recursively finding arbitrary roots of higher order arbitrary polynomials

DOI10.1016/j.amc.2004.03.028zbMath1063.65038OpenAlexW2027528539MaRDI QIDQ1765859

Zheru Chi, C. K. Law Ken, De-Shuang Huang, H. S. Ip Horace, Hau-San Wong

Publication date: 23 February 2005

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2004.03.028

zbMATH Keywords

PartitioningNumerical examplesMuller methodPolynomialLaguerre methodComplex constrained learning algorithmFeedforward neural networkJenkins-Traub methodRoots-finder

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Zeros of polynomials, rational functions, and other analytic functions of one complex variable (e.g., zeros of functions with bounded Dirichlet integral) (30C15) Numerical computation of solutions to single equations (65H05) Real polynomials: location of zeros (26C10)

Related Items (1)

A case study for constrained learning neural root finders

Cites Work

• Dilation method for finding close roots of polynomials based on constrained learning neural networks
• Phase unwrapping by factorization
• Learning representations by back-propagating errors
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