Abstract:
The numerical procedures of the fractional order kidney function model (FO-KFM) are presented in this study.
These derivatives are implemented to get the precise and accurate solutions of FO-KFM. The nonlinear form of
KFM is separated into human (infected, susceptible, recovered) and the components of water (calcium, mag
nesium). Three cases of FO-KFM are numerically accessible using the stochastic computing scaled conjugate
gradient neural networks (SCJGNNs). The statics assortment is performed to solve the FO-KFM, which is used as
78 % for verification and 11 % for both endorsement and training. The precision of SCJGNNs is achieved using
the achieved and source outcomes. The reference solutions have been obtained by using the Adam numerical
scheme. The competence, rationality, constancy is observed through the SCJGNNs accompanied by the imita
tions of state transition, regression performances, correlation, and error histograms measures.
