Gudermannian neural network procedure for the nonlinear prey-predator dynamical system
The present study performs the design of a novel Gudermannian neural networks (GNNs) for the nonlinear dynamics of prey-predator system (NDPPS). The process of GNNs is applied using the global and local search approaches named as genetic algorithm and interior-point algorithms, i.e., GNNs-GA-IPA. An...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024049211 |
| _version_ | 1827291131598077952 |
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| author | Hafsa Alkaabi Noura Alkarbi Nouf Almemari Salem Ben Said Zulqurnain Sabir |
| author_facet | Hafsa Alkaabi Noura Alkarbi Nouf Almemari Salem Ben Said Zulqurnain Sabir |
| author_sort | Hafsa Alkaabi |
| collection | DOAJ |
| description | The present study performs the design of a novel Gudermannian neural networks (GNNs) for the nonlinear dynamics of prey-predator system (NDPPS). The process of GNNs is applied using the global and local search approaches named as genetic algorithm and interior-point algorithms, i.e., GNNs-GA-IPA. An error-based merit function is constructed using the NDPPS and its initial conditions and then optimized by the hybrid of GA-IPA. Six cases of the NDPPS using the variable coefficients have been presented and the correctness is observed through the overlapping of the obtained and Runge-Kutta reference results. The results of the NDPPS have been performed between 0 and 5 using the step size 0.02. The graph of absolute error are performed around 10−06 to 10−08 to check the consistency of the proposed GNNs-GA-IPA. The statistical analysis based minimum, median and semi-interquartile ranges have been performed for both predator and prey dynamics of the model. Moreover, the investigations through the statistical operators are performed to validate the reliability of the obtained outcomes based on multiple trials. |
| first_indexed | 2024-04-24T12:31:39Z |
| format | Article |
| id | doaj.art-5147d99cab594c72a432f9bab4eef726 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T12:31:39Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-5147d99cab594c72a432f9bab4eef7262024-04-08T04:08:33ZengElsevierHeliyon2405-84402024-04-01107e28890Gudermannian neural network procedure for the nonlinear prey-predator dynamical systemHafsa Alkaabi0Noura Alkarbi1Nouf Almemari2Salem Ben Said3Zulqurnain Sabir4Department of Mathematical Sciences, College of Science, United Arab Emirates University, P. O. Box 15551, Al Ain, United Arab EmiratesDepartment of Mathematical Sciences, College of Science, United Arab Emirates University, P. O. Box 15551, Al Ain, United Arab EmiratesDepartment of Mathematical Sciences, College of Science, United Arab Emirates University, P. O. Box 15551, Al Ain, United Arab EmiratesDepartment of Mathematical Sciences, College of Science, United Arab Emirates University, P. O. Box 15551, Al Ain, United Arab Emirates; Corresponding author.Department of Mathematical Sciences, College of Science, United Arab Emirates University, P. O. Box 15551, Al Ain, United Arab Emirates; Department of Computer Science and Mathematics, Lebanese American University, Beirut, LebanonThe present study performs the design of a novel Gudermannian neural networks (GNNs) for the nonlinear dynamics of prey-predator system (NDPPS). The process of GNNs is applied using the global and local search approaches named as genetic algorithm and interior-point algorithms, i.e., GNNs-GA-IPA. An error-based merit function is constructed using the NDPPS and its initial conditions and then optimized by the hybrid of GA-IPA. Six cases of the NDPPS using the variable coefficients have been presented and the correctness is observed through the overlapping of the obtained and Runge-Kutta reference results. The results of the NDPPS have been performed between 0 and 5 using the step size 0.02. The graph of absolute error are performed around 10−06 to 10−08 to check the consistency of the proposed GNNs-GA-IPA. The statistical analysis based minimum, median and semi-interquartile ranges have been performed for both predator and prey dynamics of the model. Moreover, the investigations through the statistical operators are performed to validate the reliability of the obtained outcomes based on multiple trials.http://www.sciencedirect.com/science/article/pii/S2405844024049211Nonlinear predator-prey systemGudermannian neural networksInterior-point algorithmGenetic algorithmNumerical computing |
| spellingShingle | Hafsa Alkaabi Noura Alkarbi Nouf Almemari Salem Ben Said Zulqurnain Sabir Gudermannian neural network procedure for the nonlinear prey-predator dynamical system Heliyon Nonlinear predator-prey system Gudermannian neural networks Interior-point algorithm Genetic algorithm Numerical computing |
| title | Gudermannian neural network procedure for the nonlinear prey-predator dynamical system |
| title_full | Gudermannian neural network procedure for the nonlinear prey-predator dynamical system |
| title_fullStr | Gudermannian neural network procedure for the nonlinear prey-predator dynamical system |
| title_full_unstemmed | Gudermannian neural network procedure for the nonlinear prey-predator dynamical system |
| title_short | Gudermannian neural network procedure for the nonlinear prey-predator dynamical system |
| title_sort | gudermannian neural network procedure for the nonlinear prey predator dynamical system |
| topic | Nonlinear predator-prey system Gudermannian neural networks Interior-point algorithm Genetic algorithm Numerical computing |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024049211 |
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