Soft Computing Paradigms to Find the Numerical Solutions of a Nonlinear Influenza Disease Model

The aim of this work is to present the numerical results influenza disease nonlinear system using feed forward artificial neural networks (ANNs) along with optimization combination global and local search schemes. genetic algorithm (GA) active-set method (ASM), i.e., GA-ASM, are implemented as mathematical dependent four classes, susceptible S(u), infected I(u), recovered R(u) cross-immune individuals C(u). For solutions these classes based on system, design an objective function presented differential equations its corresponding initial conditions. hybrid computing GA-ASM for solving all system. obtained will be compared by Adams check authenticity designed ANN-GA-ASM. In addition, approach through statistical operators shows consistency stability