Intelligence computing approach for solving second order system of Emden–Fowler model

In this research study, an advance computational intelligence paradigm is used for solving second order Emden-Fowler system (EFS) based on artificial neural network, genetic algorithm (GA) which is a famous global search method, sequential quadratic programming (SQP) known as rapid local refinement and the hybrid of GA-SQP. The proficiency of the designed scheme is inspected by solving the three examples of EFS to check the efficiency, consistency, precision and exactness of the technique. The numerical outcomes of the purposed scheme are compared with the exact solution that shows the significance of the scheme based on accuracy, correctness and convergence. Moreover, statistical explorations have been executed to verify the precision and accuracy of the outcomes based on performance measures of mean absolute deviation, root mean squared error and variance account for.