Heuristic computational design of Morlet wavelet for solving the higher order singular nonlinear differential equations
The aim of this study is to present the numerical solutions of the higher order singular nonlinear differential equations using an advanced intelligent computational approach by manipulating the Morlet wavelet (MW) neural networks (NNs), global approach as genetic algorithm (GA) and quick local sear...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S111001682100243X |
| _version_ | 1828924143299461120 |
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| author | Zulqurnain Sabir Kashif Nisar Muhammad Asif Zahoor Raja Ag. Asri Bin Ag. Ibrahim Joel J.P.C. Rodrigues K.S. Al-Basyouni S.R. Mahmoud Danda B. Rawat |
| author_facet | Zulqurnain Sabir Kashif Nisar Muhammad Asif Zahoor Raja Ag. Asri Bin Ag. Ibrahim Joel J.P.C. Rodrigues K.S. Al-Basyouni S.R. Mahmoud Danda B. Rawat |
| author_sort | Zulqurnain Sabir |
| collection | DOAJ |
| description | The aim of this study is to present the numerical solutions of the higher order singular nonlinear differential equations using an advanced intelligent computational approach by manipulating the Morlet wavelet (MW) neural networks (NNs), global approach as genetic algorithm (GA) and quick local search approach as interior-point method (IPM), i.e., GA-IPM. MWNNs is applied to discretize the higher order singular nonlinear differential equations to express the activation function using the mean square error. The performance of the designed MWNNs using the GA-IPM is observed to solve three different variants based on the higher order singular nonlinear differential model to check the significance, efficacy and consistency of the designed MWNNs using the GA-IPM. Furthermore, statistical performances are provided to check the precision, accuracy and convergence of the present approach. |
| first_indexed | 2024-12-13T22:51:42Z |
| format | Article |
| id | doaj.art-e2bf5d246e9d4b7b8c2044f34ea5e2b1 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-12-13T22:51:42Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-e2bf5d246e9d4b7b8c2044f34ea5e2b12022-12-21T23:28:37ZengElsevierAlexandria Engineering Journal1110-01682021-12-0160659355947Heuristic computational design of Morlet wavelet for solving the higher order singular nonlinear differential equationsZulqurnain Sabir0Kashif Nisar1Muhammad Asif Zahoor Raja2Ag. Asri Bin Ag. Ibrahim3Joel J.P.C. Rodrigues4K.S. Al-Basyouni5S.R. Mahmoud6Danda B. Rawat7Department of Mathematics and Statistics, Hazara University, Mansehra, PakistanFaculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, 88400 KK, Malaysia; Corresponding author.Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, ROCFaculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, 88400 KK, MalaysiaFederal University of Piauí (UFPI), Teresina - PI, Brazil; Instituto de Telecomunicações, 6201-001 Covilhã, PortugalMathematics Department, Faculty of Science, King Abdulaziz University, Jeddah, Saudi ArabiaGRC Department, Faculty of Applied Studies, King Abdulaziz University, Jeddah, Saudi ArabiaData Science and Cybersecurity Center, Dept of Electrical Engineering and Computer Science, Howard University, Washington, DC, USAThe aim of this study is to present the numerical solutions of the higher order singular nonlinear differential equations using an advanced intelligent computational approach by manipulating the Morlet wavelet (MW) neural networks (NNs), global approach as genetic algorithm (GA) and quick local search approach as interior-point method (IPM), i.e., GA-IPM. MWNNs is applied to discretize the higher order singular nonlinear differential equations to express the activation function using the mean square error. The performance of the designed MWNNs using the GA-IPM is observed to solve three different variants based on the higher order singular nonlinear differential model to check the significance, efficacy and consistency of the designed MWNNs using the GA-IPM. Furthermore, statistical performances are provided to check the precision, accuracy and convergence of the present approach.http://www.sciencedirect.com/science/article/pii/S111001682100243XMorlet wavelet neural networksNonlinear higher order modelMulti-Singular, Statistical measuresArtificial neural networksInterior-point methodGenetic algorithm |
| spellingShingle | Zulqurnain Sabir Kashif Nisar Muhammad Asif Zahoor Raja Ag. Asri Bin Ag. Ibrahim Joel J.P.C. Rodrigues K.S. Al-Basyouni S.R. Mahmoud Danda B. Rawat Heuristic computational design of Morlet wavelet for solving the higher order singular nonlinear differential equations Alexandria Engineering Journal Morlet wavelet neural networks Nonlinear higher order model Multi-Singular, Statistical measures Artificial neural networks Interior-point method Genetic algorithm |
| title | Heuristic computational design of Morlet wavelet for solving the higher order singular nonlinear differential equations |
| title_full | Heuristic computational design of Morlet wavelet for solving the higher order singular nonlinear differential equations |
| title_fullStr | Heuristic computational design of Morlet wavelet for solving the higher order singular nonlinear differential equations |
| title_full_unstemmed | Heuristic computational design of Morlet wavelet for solving the higher order singular nonlinear differential equations |
| title_short | Heuristic computational design of Morlet wavelet for solving the higher order singular nonlinear differential equations |
| title_sort | heuristic computational design of morlet wavelet for solving the higher order singular nonlinear differential equations |
| topic | Morlet wavelet neural networks Nonlinear higher order model Multi-Singular, Statistical measures Artificial neural networks Interior-point method Genetic algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S111001682100243X |
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