A design of novel Gudermannian neural networks for the nonlinear multi-pantograph delay differential singular model

Abstract

In this paper, a new stochastic numerical platform through Gudermannian neural network (GNN) based intelligent computing solver (GNNICS) is accessible for solving the nonlinear singular multi-pantograph delay differential (MP-DD) systems. In GNNICS, Gudermannian kernel is exploited to construct the neural network models of differential operators with different neurons for the nonlinear system and hybrid computing via global genetic algorithm (GA) and local refinements with active set (AS), i.e., GNN-GAAS method. A fitness function with GNN models is formulated for solving the MP-DD equation and optimization of the design variables of the network is performed with GAAS. To investigate the performance of the designed GNNICS based on GNN-GAAS algorithm, three different variants of the MP-DD systems are used to assess the correctness, effectiveness, and robustness. The statistical investigations based on different performance metrics have been presented to authenticate the consistent accuracy, convergence, and stability of the designed GNN-GAAS algorithm.

An artificial neural network approach for the language learning model

“…The current investigations present the solutions of learning language differential model by using the artificial intelligence (AI) procedure of scale conjugate gradient neural network (SCJGNN). In recent years, the stochastic computing solvers have been exploited in various applications, like susceptible, exposed, infected and recovered nonlinear model based on the worms propagation using the networks of wireless sensor 28 , eye surgery differential system 29 , 30 , predictive networks for the delayed model of tumor-immune 31 , HIV infection system 32 , 33 , delay differential model based avian influenza 34 , Lane-Emden model 35 , 36 , influenza-A epidemic system 37 , smoking model 38 , 39 , multi-delayed model based tumor oncolytic virotherapy 40 , thermal explosion model 41 , and biological differential system 42 . Some novel features of this research are presented as: The solutions of the learning language differential model using the stochastic AI along with the SCJGNN solver are presented successfully.…”