An artificial neural network based on radial basis methodology using delay effects in the Parkinson's disease model

The purpose of this study is to find the numerical solutions of the delay Parkinson's disease model by employing a computing neural network framework. The disease model is divided into five components: healthy brain neurons, infected brain neurons, activated microglia cells, extracellular α-synuclein, and the activated T-cell population. A two-layered neural network scheme using radial basis functions in both hidden layers, and twelve and twenty neurons in layer-1 and layer-2 for solving the Parkinson's disease model. A dataset is obtained using the implicit Runge-Kutta method, which is trained by the Bayesian regularization taking reasonable percentages of training, testing and validation. The objective of this research is to minimize the mean square error using the proposed two-layered neural network structure. The absolute error values ranged from 10^-07 to 10^-09 confirming the accuracy of the designed technique. In addition, the optimal training between 10^-11 to 10^-13, along with error histograms, regression analysis, and state transition plots, validates the accuracy of the proposed scheme.