Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25965
Title: A case study for constrained learning neural root finders
Authors: Huang, DS
Chi, Z 
Siu, WC 
Keywords: Computational complexity
Constrained learning algorithm
Feedforward neural networks
Finding Roots
Polynomials
Recursive partitioning
Issue Date: 2005
Publisher: Elsevier
Source: Applied mathematics and computation, 2005, v. 165, no. 3, p. 699-718 How to cite?
Journal: Applied mathematics and computation 
Abstract: 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.
URI: http://hdl.handle.net/10397/25965
ISSN: 0096-3003
EISSN: 1873-5649
DOI: 10.1016/j.amc.2004.04.070
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