A case study for constrained learning neural root finders

De Shuang Huang, Zheru Chi, Wan Chi Siu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

21 Citations (Scopus)


This paper makes the detailed analyses of computational complexities and related parameters selection on our proposed constrained learning neural network root-finders including the original feedforward neural network root-finder (FNN-RF) and the recursive partitioning feedforward neural network root-finder (RP-FNN-RF). Specifically, we investigate the case study of the CLA used in neural root-finders (NRF), including the effects of different parameters with the CLA on the NRF. Finally, several computer simulation results demonstrate the performance of our proposed approach and support our claims.
Original languageEnglish
Pages (from-to)699-718
Number of pages20
JournalApplied Mathematics and Computation
Issue number3
Publication statusPublished - 27 Jun 2005


  • Computational complexity
  • Constrained learning algorithm
  • Feedforward neural networks
  • Finding Roots
  • Polynomials
  • Recursive partitioning

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics


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