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.