Fractional Order Nonlinear Bone Remodeling Dynamics Using the Supervised Neural Network

This study aims to solve the nonlinear fractional-order mathematical model (FOMM) by using normal and dysregulated bone remodeling of myeloma disease (MBD). For more precise performance model, derivatives have been used numerically. The FOMM is preliminarily designed focus on critical interactions between resorption or osteoclasts (OC) formation osteoblasts (OB). connections OC OB are represented a differential system based cellular components, which depict stable fluctuation in usual case unstable through MBD. Untreated causes increasing reducing osteoblasts, resulting net waste tumor growth. solutions will be provided stochastic framework Levenberg-Marquardt backpropagation (LVMBP) neural networks (NN), i.e., LVMBPNN. performances three variations derivative LVMPNN. static structural 82% for investigation 9% both learning certification. LVMBPNN authenticated results Adams-Bashforth-Moulton mechanism. To accomplish capability, steadiness, accuracy, ability LVMBPNN, error histograms (EHs), mean square (MSE), recurrence, state transitions (STs) provided.