Design of neuro-swarming computational solver for the fractional Bagley–Torvik mathematical model
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Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This paper has been partially supported by Fundación Séneca de la Región de Murcia grant numbers 20783/PI/18, and Ministerio de Ciencia, Innovación y Universidades grant number PGC2018-0971-B-100.Realizado en/con
Universidad Politécnica de CartagenaPublication date
2022-02-18Publisher
SPRINGERBibliographic Citation
Guirao, J.L.G., Sabir, Z., Raja, M.A.Z. et al. Design of neuro-swarming computational solver for the fractional Bagley–Torvik mathematical model. Eur. Phys. J. Plus 137, 245 (2022). https://doi.org/10.1140/epjp/s13360-022-02421-3Peer review
SIAbstract
This 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.
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