Dynamic Analysis of an Economic and Financial Supply Chain System Using the Supervised Neural Networks

This study presents the dynamic analysis for the fractional order economic and financial supply chain dynamical system using the supervised neural network performances aided with scale conjugate gradient. The investigations based on the fractional derivatives have been implemented to achieve the realistic and accurate performances of the fractional order economic and financial supply chain dynamical system. The mathematical form of the fractional order economic and financial supply chain dynamical system is categorized into three dynamics: rate of interest, investment cost, and price index. Three different variations based on the fractional order form of the economic and financial supply chain dynamical system have been numerically presented using the supervised neural network performances based on the scale conjugate gradient scheme. The selection of the data for solving the economic and financial supply chain dynamical system is taken as 80% for training, and 12% for testing, and 8% for endorsement. The accuracy of the proposed stochastic scheme is presented using the obtained and referenced Adam results. Rationality, capability and perfection are performed through the supervised neural network with scale conjugate gradient scheme performances-based together with the performances of correlation/regression, mean square error, state transition measures, and error histograms. Finally, a comparison of numerical results is examined and observed that the range of absolute error is between
$10^{-05}$
to
$10^{-07}$
which indicates that the proposed stochastic computing model can effectively analyze the economic and financial supply chain dynamical system.