| Autor: |
Sabir Z; Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan., Wahab HA; Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan., Guirao JLG; Department of Applied Mathematics and Statistics, Technical University of Cartagena, Hospital de Marina 30203-Cartagena, Spain.; Nonlinear Analysis and Applied Mathematics (NAAM)-Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, P. O. Box 80203, Jeddah 21589, Saudi Arabia. |
| Jazyk: |
angličtina |
| Zdroj: |
Mathematical biosciences and engineering : MBE [Math Biosci Eng] 2022 Jan; Vol. 19 (1), pp. 663-687. Date of Electronic Publication: 2021 Nov 19. |
| DOI: |
10.3934/mbe.2022030 |
| Abstrakt: |
The present work is to solve the nonlinear singular models using the framework of the stochastic computing approaches. The purpose of these investigations is not only focused to solve the singular models, but the solution of these models will be presented to the extended form of the delayed, prediction and pantograph differential models. The Gudermannian function is designed using the neural networks optimized through the global scheme "genetic algorithms (GA)", local method "sequential quadratic programming (SQP)" and the hybridization of GA-SQP. The comparison of the singular equations will be presented with the exact solutions along with the extended form of delayed, prediction and pantograph based on these singular models. Moreover, the neuron analysis will be provided to authenticate the efficiency and complexity of the designed approach. For the correctness and effectiveness of the proposed approach, the plots of absolute error will be drawn for the singular delayed, prediction and pantograph differential models. For the reliability and stability of the proposed method, the statistical performances "Theil inequality coefficient", "variance account for" and "mean absolute deviation'' are observed for multiple executions to solve singular delayed, prediction and pantograph differential models. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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