Author:
Guirao Juan L. G.,Sabir Zulqurnain,Raja Muhammad Asif Zahoor,Baleanu Dumitru
Abstract
AbstractThis study is to introduce a novel design and implementation of a neuro-swarming computational numerical procedure for numerical treatment of the fractional Bagley–Torvik mathematical model (FBTMM). The optimization procedures based on the global search with particle swarm optimization (PSO) and local search via active-set approach (ASA), while Mayer wavelet kernel-based activation function used in neural network (MWNNs) modeling, i.e., MWNN-PSOASA, to solve the FBTMM. The efficiency of the proposed stochastic solver MWNN-GAASA is utilized to solve three different variants based on the fractional order of the FBTMM. For the meticulousness of the stochastic solver MWNN-PSOASA, the obtained and exact solutions are compared for each variant of the FBTMM with reasonable accuracy. For the reliability of the stochastic solver MWNN-PSOASA, the statistical investigations are provided based on the stability, robustness, accuracy and convergence metrics.
Funder
Ministerio de Ciencia, Innovación y Universidades
Fundación Séneca
Universidad Politécnica de Cartagena
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy
Cited by
18 articles.
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