Morlet Wavelet Neural Network Investigations to Present the Numerical Investigations of the Prediction Differential Model
In this study, a design of Morlet wavelet neural networks (MWNNs) is presented to solve the prediction differential model (PDM) by applying the global approximation capability of a genetic algorithm (GA) and local quick interior-point algorithm scheme (IPAS), i.e., MWNN-GAIPAS. The famous and histor...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/11/21/4480 |
| _version_ | 1827765603810672640 |
|---|---|
| author | Zulqurnain Sabir Adnène Arbi Atef F. Hashem Mohamed A Abdelkawy |
| author_facet | Zulqurnain Sabir Adnène Arbi Atef F. Hashem Mohamed A Abdelkawy |
| author_sort | Zulqurnain Sabir |
| collection | DOAJ |
| description | In this study, a design of Morlet wavelet neural networks (MWNNs) is presented to solve the prediction differential model (PDM) by applying the global approximation capability of a genetic algorithm (GA) and local quick interior-point algorithm scheme (IPAS), i.e., MWNN-GAIPAS. The famous and historical PDM is known as a variant of the functional differential system that works as theopposite of the delay differential models. A fitness function is constructed by using the mean square error and optimized through the GA-IPAS for solving the PDM. Three PDM examples have been presented numerically to check the authenticity of the MWNN-GAIPAS. For the perfection of the designed MWNN-GAIPAS, the comparability of the obtained outputs and exact results is performed. Moreover, the neuron analysis is performed by taking 3, 10, and 20 neurons. The statistical observations have been performed to authenticate the reliability of the MWNN-GAIPAS for solving the PDM. |
| first_indexed | 2024-03-11T11:24:56Z |
| format | Article |
| id | doaj.art-01d13843c4f7426da673002e2f198402 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T11:24:56Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-01d13843c4f7426da673002e2f1984022023-11-10T15:08:01ZengMDPI AGMathematics2227-73902023-10-011121448010.3390/math11214480Morlet Wavelet Neural Network Investigations to Present the Numerical Investigations of the Prediction Differential ModelZulqurnain Sabir0Adnène Arbi1Atef F. Hashem2Mohamed A Abdelkawy3Department of Computer Science and Mathematics, Lebanese American University, Beirut 1401, LebanonLaboratory of Engineering Mathematics (LR01ES13), Tunisia Polytechnic School, University of Carthage, Tunis 2078, TunisiaDepartment of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi ArabiaDepartment of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi ArabiaIn this study, a design of Morlet wavelet neural networks (MWNNs) is presented to solve the prediction differential model (PDM) by applying the global approximation capability of a genetic algorithm (GA) and local quick interior-point algorithm scheme (IPAS), i.e., MWNN-GAIPAS. The famous and historical PDM is known as a variant of the functional differential system that works as theopposite of the delay differential models. A fitness function is constructed by using the mean square error and optimized through the GA-IPAS for solving the PDM. Three PDM examples have been presented numerically to check the authenticity of the MWNN-GAIPAS. For the perfection of the designed MWNN-GAIPAS, the comparability of the obtained outputs and exact results is performed. Moreover, the neuron analysis is performed by taking 3, 10, and 20 neurons. The statistical observations have been performed to authenticate the reliability of the MWNN-GAIPAS for solving the PDM.https://www.mdpi.com/2227-7390/11/21/4480Morlet wavelet kernelprediction differential systemgenetic algorithmdelay differential systeminterior-point algorithm scheme |
| spellingShingle | Zulqurnain Sabir Adnène Arbi Atef F. Hashem Mohamed A Abdelkawy Morlet Wavelet Neural Network Investigations to Present the Numerical Investigations of the Prediction Differential Model Mathematics Morlet wavelet kernel prediction differential system genetic algorithm delay differential system interior-point algorithm scheme |
| title | Morlet Wavelet Neural Network Investigations to Present the Numerical Investigations of the Prediction Differential Model |
| title_full | Morlet Wavelet Neural Network Investigations to Present the Numerical Investigations of the Prediction Differential Model |
| title_fullStr | Morlet Wavelet Neural Network Investigations to Present the Numerical Investigations of the Prediction Differential Model |
| title_full_unstemmed | Morlet Wavelet Neural Network Investigations to Present the Numerical Investigations of the Prediction Differential Model |
| title_short | Morlet Wavelet Neural Network Investigations to Present the Numerical Investigations of the Prediction Differential Model |
| title_sort | morlet wavelet neural network investigations to present the numerical investigations of the prediction differential model |
| topic | Morlet wavelet kernel prediction differential system genetic algorithm delay differential system interior-point algorithm scheme |
| url | https://www.mdpi.com/2227-7390/11/21/4480 |
| work_keys_str_mv | AT zulqurnainsabir morletwaveletneuralnetworkinvestigationstopresentthenumericalinvestigationsofthepredictiondifferentialmodel AT adnenearbi morletwaveletneuralnetworkinvestigationstopresentthenumericalinvestigationsofthepredictiondifferentialmodel AT ateffhashem morletwaveletneuralnetworkinvestigationstopresentthenumericalinvestigationsofthepredictiondifferentialmodel AT mohamedaabdelkawy morletwaveletneuralnetworkinvestigationstopresentthenumericalinvestigationsofthepredictiondifferentialmodel |