Design of Morlet wavelet neural network to solve the non-linear influenza disease system

In this study, the solution of the non-linear influenza disease system (NIDS) is presented using the Morlet wavelet neural networks (MWNNs) together with the optimisation procedures of the hybrid process of global/local search approaches. The genetic algorithm (GA) and sequential quadratic programmi...

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Veröffentlicht in:Applied mathematics and nonlinear sciences Jg. 8; H. 1; S. 2033 - 2048
Hauptverfasser: Sabir, Zulqurnain, Umar, Muhammad, Raja, Muhammad Asif Zahoor, Fathurrochman, Irwan, Shorman, Samer M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Beirut Sciendo 01.01.2023
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Schlagworte:
genetic algorithms
Influenza
Morlet wavelet neural networks
Neural networks
Non-linear influenza disease system
numerical measures
Runge–Kutta
sequential quadratic programming
ISSN:2444-8656, 2444-8656
Online-Zugang:Volltext
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Zusammenfassung:In this study, the solution of the non-linear influenza disease system (NIDS) is presented using the Morlet wavelet neural networks (MWNNs) together with the optimisation procedures of the hybrid process of global/local search approaches. The genetic algorithm (GA) and sequential quadratic programming (SQP), that is, GA-SQP, are executed as the global and local search techniques. The mathematical form of the NIDS depends upon four groups: susceptible ), infected ), recovered ) and cross-immune individuals ). To solve the NIDS, an error function is designed using NIDS and its leading initial conditions (ICs). This error function is optimised with a combination of MWNNs and GA-SQP to solve for all the groups of NIDS. The comparison of the obtained solutions and Runge–Kutta results is presented to authenticate the correctness of the designed MWNNs along with the GA-SQP for solving NIDS. Moreover, the statistical operators using different measures are presented to check the reliability and constancy of the MWNNs along with the GA-SQP to solve the NIDS.
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ISSN:2444-8656
2444-8656
DOI:10.2478/amns.2021.2.00120