A bio inspired learning scheme for the fractional order kidney function model with neural networks

Sabir Z. Bhat S.A. Wahab H.A. Camargo M.E. Abildinova G. Zulpykhar Z.
March 2024 Elsevier Ltd

Chaos, Solitons and Fractals
2024 #180

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, magnesium). 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 imitations of state transition, regression performances, correlation, and error histograms measures.

Fractional order , Kidney function model , Neural networks , Numerical results , Scaled conjugate gradient

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Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
LUT Business School, LUT University, P.O. Box 20, Lappeenranta, FIN-53851, Finland
Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan
Graduate Program in Administration, Federal University of Santa Maria, Santa Maria, 93458, Brazil
L.N. Gumilyov Eurasian National University, Department of Computer Science, Astana, Kazakhstan

Department of Computer Science and Mathematics
LUT Business School
Department of Mathematics and Statistics
Graduate Program in Administration
L.N. Gumilyov Eurasian National University

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