A case study for constrained learning neural root finders

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. 2004 Elsevier Inc. All rights reserved.