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...

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Bibliographic Details
Published in:Mathematics (Basel) Vol. 11; no. 21; p. 4480
Main Authors: Sabir, Zulqurnain, Arbi, Adnène, Hashem, Atef F., Abdelkawy, Mohamed A
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2023
Subjects:
Algorithms
Approximation
delay differential system
Design
genetic algorithm
Genetic algorithms
interior-point algorithm scheme
Mathematical models
Morlet wavelet
Morlet wavelet kernel
Neural networks
Neurons
Optimization
prediction differential system
ISSN:2227-7390, 2227-7390
Online Access:Get full text
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Summary: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.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math11214480