Morlet Wavelet Neural Network Investigations to Present the Numerical Investigations of the Prediction Differential Model

Zulqurnain Sabir, Adnène Arbi (), Atef F. Hashem and Mohamed A Abdelkawy
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Zulqurnain Sabir: Department of Computer Science and Mathematics, Lebanese American University, Beirut 1401, Lebanon
Adnène Arbi: Laboratory of Engineering Mathematics (LR01ES13), Tunisia Polytechnic School, University of Carthage, Tunis 2078, Tunisia
Atef F. Hashem: Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
Mohamed A Abdelkawy: Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia

Mathematics, 2023, vol. 11, issue 21, 1-20

Abstract: 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.

Keywords: Morlet wavelet kernel; prediction differential system; genetic algorithm; delay differential system; interior-point algorithm scheme (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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