<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>The aim of this work is to solve a mathematical model based on the migration and emigration effects. The designed mathematical model shows one of the forms of prey-predator. The migration factor represents a step function for both normal and individuals that is restrictions or movement of people. The numerical solutions of the designed model are presented using the stochastic computational schemes based on the artificial neural networks (ANNs) together with the Levenberg-Marquardt back propagation (LMB), i.e., ANNs-LMB for solving the model based on the migration and emigration effects. Three different cases have been performed to solve the model based on the migration and emigration effects with the ANNs-LMB solver in terms of authentication, training, sample statistics and testing. The selection of the data is chosen as 80%, 10%, 10% for training, testing and authentication, respectively. The numerical results through the ANNs-LMB of the model based on the migration and emigration effects will be compared with the Runge-Kutta method. The results of the model based on the migration and emigration effects using the ANNs-LMB are provided to reduce the mean square error (MSE). For the capability and efficiency of the proposed ANNs-LMB, the numerical results are provided using the correlation, error histograms, regression and MSE.</p>
</abstract>

The aim of this work is to solve a mathematical model based on the migration and emigration effects. The designed mathematical model shows one of the forms of prey-predator. The migration factor represents a step function for both normal and individuals that is restrictions or movement of people. The numerical solutions of the designed model are presented using the stochastic computational schemes based on the artificial neural networks (ANNs) together with the Levenberg-Marquardt back propagation (LMB), i.e., ANNs-LMB for solving the model based on the migration and emigration effects. Three different cases have been performed to solve the model based on the migration and emigration effects with the ANNs-LMB solver in terms of authentication, training, sample statistics and testing. The selection of the data is chosen as 80%, 10%, 10% for training, testing and authentication, respectively. The numerical results through the ANNs-LMB of the model based on the migration and emigration effects will be compared with the Runge-Kutta method. The results of the model based on the migration and emigration effects using the ANNs-LMB are provided to reduce the mean square error (MSE). For the capability and efficiency of the proposed ANNs-LMB, the numerical results are provided using the correlation, error histograms, regression and MSE.

On the stochastic observation for the nonlinear system of the emigration and migration effects via artificial neural networks

Departamento de Matemática Aplicada y Estadística

Universidad Politécnica de Cartagena, Hospital de Marina

International Journal of Mathematics and Computer in Engineering

This work is licensed under the Creative Commons Attribution 4.0 International License.

The aim of this work is to solve a mathematical model based on the migration and emigration effects. The designed mathematical model shows one of the forms of...

On the stochastic observation for the nonlinear system of the emigration and migration effects via artificial neural networks

Online veröffentlicht: 13. Sept. 2023

Seitenbereich: 177 - 186

Eingereicht: 05. Juni 2023

Akzeptiert: 06. Aug. 2023

DOI: https://doi.org/10.2478/ijmce-2023-0014

Schlüsselwörter
A mathematical model, migration and emigration effects, artificial neural network, numerical results, Runge-Kutta scheme

© 2023 Juan Luis García Guirao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

On the stochastic observation for the nonlinear system of the emigration and migration effects via artificial neural networks

Online veröffentlicht: 13. Sept. 2023

Seitenbereich: 177 - 186

Eingereicht: 05. Juni 2023

Akzeptiert: 06. Aug. 2023

DOI: https://doi.org/10.2478/ijmce-2023-0014

SchlüsselwörterA mathematical model, migration and emigration effects, artificial neural network, numerical results, Runge-Kutta scheme

© 2023 Juan Luis García Guirao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
A mathematical model, migration and emigration effects, artificial neural network, numerical results, Runge-Kutta scheme