A case study for constrained learning neural root finders

DOI10.1016/j.amc.2004.04.070zbMath1075.68073OpenAlexW1994217508MaRDI QIDQ1780526

De-Shuang Huang, Wan-Chi Siu, Zheru Chi

Publication date: 13 June 2005

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

Full work available at URL: https://doi.org/10.1016/j.amc.2004.04.070

zbMATH Keywords

Computational complexityPolynomialsFeedforward neural networksRecursive partitioningConstrained learning algorithmFinding Roots

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• A new partitioning neural network model for recursively finding arbitrary roots of higher order arbitrary polynomials
• Dilation method for finding close roots of polynomials based on constrained learning neural networks
• Phase unwrapping by factorization
• A Steepest-Ascent Method for Solving Optimum Programming Problems
• A geometric approach to root finding in GT(q/sup m/)
• A Neural Root Finder of Polynomials Based on Root Moments
• Learning representations by back-propagating errors

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