FRACTIONAL MEYER NEURAL NETWORK PROCEDURES OPTIMIZED BY THE GENETIC ALGORITHM TO SOLVE THE BAGLEY-TORVIK MODEL

Zulqurnain Sabir; Muhammad Asif Zahoor Raja; R. Sadat; Khaled. S. Ahmed; Mohamed R. Ali; Wael Al-Kouz

doi:10.11948/20220019

2022 Volume 12 Issue 6

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Zulqurnain Sabir, Muhammad Asif Zahoor Raja, R. Sadat, Khaled. S. Ahmed, Mohamed R. Ali, Wael Al-Kouz. FRACTIONAL MEYER NEURAL NETWORK PROCEDURES OPTIMIZED BY THE GENETIC ALGORITHM TO SOLVE THE BAGLEY-TORVIK MODEL[J]. Journal of Applied Analysis & Computation, 2022, 12(6): 2458-2474. doi: 10.11948/20220019

Citation:

Zulqurnain Sabir, Muhammad Asif Zahoor Raja, R. Sadat, Khaled. S. Ahmed, Mohamed R. Ali, Wael Al-Kouz. FRACTIONAL MEYER NEURAL NETWORK PROCEDURES OPTIMIZED BY THE GENETIC ALGORITHM TO SOLVE THE BAGLEY-TORVIK MODEL[J]. Journal of Applied Analysis & Computation, 2022, 12(6): 2458-2474. doi: 10.11948/20220019

FRACTIONAL MEYER NEURAL NETWORK PROCEDURES OPTIMIZED BY THE GENETIC ALGORITHM TO SOLVE THE BAGLEY-TORVIK MODEL

1.
Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan
2.
Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, R.O.C.
3.
Department of Mathematics, Zagazig Faculty of Engineering, Zagazig University, Egypt
4.
Benha Faculty of Engineering, Benha University, Egypt
5.
Center of Research, Faculty of Engineering and Technology, Future University in Egypt New Cairo, 11835, Egypt
6.
College of Engineering and Technology, American University of the Middle East, Kuwait

Corresponding author: Email: mohamed.reda@bhit.bu.edu.eg(M. R. Ali)

Abstract

Abstract

The current investigations are related to indicate a competent numerical fractional Meyer neural network (FMNN) procedure using the optimization of genetic algorithm and interior-point algorithm (GAIPA), i.e., FMNN-GAIPA for solving the Bagley--Torvik model (BTM). A merit function based on the differential BTM form, and its corresponding initial conditions is constructed and then optimized with the FMNN-GAIPA. Three different BTM cases will be solved through the FMNN-GAIPA and the correctness of the proposed FMNN-GAIPA is observed by using the comparison for each case of the BTM with the exact solutions. The statistical investigations based on the appropriate large independent trials recognized the constancy of the FMNN-GAIPA in terms of robustness, convergence, and stability trials. In addition, the annotations over the statistical measures validate the values of FMNN-GAIPA.
- Fractional Meyer neural network /
- Bagley--Torvik model /
- genetic algorithm /
- statistical analysis /
- interior-point algorithm
MSC: 26A33, 90B15, 90B10, 68M10

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References

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