Study of Nonlinear Models of Oscillatory Systems by Applying an Intelligent Computational Technique
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Date
2021-12
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Abstract
In 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.
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Damping, Large amplitude, Levenberg–Marquardt algorithm, Mass attached to a stretched elastic wire, Neural networks, Nonlinear oscillator, Runge–Kutta method, Soft computing
Citation
Khan, 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