| Abstrakt: |
The purpose behind this research is to utilize the knack of Bayesian solver to determine numerical solution of functional differential equations arising in the quantum calculus models. Functional differential equations having discrete versions are very difficult to solve due to the presence of delay term, here with the implementation of Bayesian solver with means of neural networks, an efficient technique has been developed to overcome the complication in the model. First, the functional differential systems are converted into recurrence relations, then datasets are generated for converted recurrence relations to construct continuous mapping for neural networks. Second, the approximate solutions are determined through employing training and testing steps on generated datasets to learn the neural networks. Furthermore, comprehensive statistical analysis are presented by applying various statistical operators such as, mean squared error (MSE), regression analysis to confirm both accuracy as well as stability of the proposed technique. Moreover, its rapid convergence and reliability is also endorsed by the histogram, training state and correlation plots. Expected level for accuracy of suggested technique is further endorsed with the comparison of attained results with the reference solution. Additionally, accuracy and reliability is also confirmed by absolute error analysis. [ABSTRACT FROM AUTHOR] |
