Study of Nonlinear Models of Oscillatory Systems by Applying an Intelligent Computational Technique

dc.contributor.authorKhan, Naveed Ahmad
dc.contributor.authorAlshammari, Fahad Sameer
dc.contributor.authorRomero, Carlos Andrés Tavera
dc.contributor.authorSulaiman, Muhammad
dc.date.accessioned2025-04-04T16:09:03Z
dc.date.available2025-04-04T16:09:03Z
dc.date.issued2021-12
dc.description.abstractIn this paper, we have analyzed the mathematical model of various nonlinear oscillators arising in different fields of engineering. Further, approximate solutions for different variations in oscillators are studied by using feedforward neural networks (NNs) based on the backpropagated Levenberg–Marquardt algorithm (BLMA). A data set for different problem scenarios for the super-vised learning of BLMA has been generated by the Runge–Kutta method of order 4 (RK-4) with the “NDSolve” package in Mathematica. The worth of the approximate solution by NN-BLMA is attained by employing the processing of testing, training, and validation of the reference data set. For each model, convergence analysis, error histograms, regression analysis, and curve fitting are considered to study the robustness and accuracy of the design scheme.
dc.identifier.citationKhan, N. A., Alshammari, F. S., Romero, C. A. T., & Sulaiman, M. (2021). Study of Nonlinear Models of Oscillatory Systems by Applying an Intelligent Computational Technique. Entropy, 23(12). https://doi.org/10.3390/e23121685
dc.identifier.issn10994300
dc.identifier.urihttps://repositorio.usc.edu.co/handle/20.500.12421/6281
dc.language.isoen
dc.subjectDamping
dc.subjectLarge amplitude
dc.subjectLevenberg–Marquardt algorithm
dc.subjectMass attached to a stretched elastic wire
dc.subjectNeural networks
dc.subjectNonlinear oscillator
dc.subjectRunge–Kutta method
dc.subjectSoft computing
dc.titleStudy of Nonlinear Models of Oscillatory Systems by Applying an Intelligent Computational Technique
dc.typeArticle

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