Gudermannian neural networks using the optimization procedures of genetic algorithm and active set approach for the three-species food chain nonlinear model

Zulqurnain Sabir; Mohamed R Ali; R Sadat

doi:10.1007/s12652-021-03638-3

Gudermannian neural networks using the optimization procedures of genetic algorithm and active set approach for the three-species food chain nonlinear model

J Ambient Intell Humaniz Comput. 2023;14(7):8913-8922. doi: 10.1007/s12652-021-03638-3. Epub 2022 Jan 18.

Authors

Zulqurnain Sabir¹, Mohamed R Ali^{2

3}, R Sadat⁴

Affiliations

¹ Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan.
² Faculty of Engineering and Technology, Future University, Cairo, Egypt.
³ Department of Basic Science, Faculty of Engineering at Benha, Benha University, Benha, 13512 Egypt.
⁴ Department of Physics and Engineering Mathematics, Faculty of Engineering, Zagazig University, Zagazig, Egypt.

Abstract

The present study is to investigate the Gudermannian neural networks (GNNs) using the optimization procedures of genetic algorithm and active-set approach (GA-ASA) to solve the three-species food chain nonlinear model. The three-species food chain nonlinear model is dependent upon the prey populations, top-predator, and specialist predator. The design of an error-based fitness function is presented using the sense of the three-species food chain nonlinear model and its initial conditions. The numerical results of the model have been obtained by exploiting the GNN-GA-ASA. The obtained results through the GNN-GA-ASA have been compared with the Runge-Kutta method to substantiate the correctness of the designed approach. The reliability, efficacy and authenticity of the proposed GNN-GA-ASA are examined through different statistical measures based on single and multiple neurons for solving the three-species food chain nonlinear model.

Keywords: Active-set algorithm; Gudermannian neural network; Nonlinear differential system; Runge–Kutta scheme; Statistical studies; Three-dimensional food chain nonlinear model.