Design of a Computational Heuristic to Solve the Nonlinear Li?nard Differential Model
Yazarlar (6)
Honghe University, Çin
Hazara University Pakistan, Pakistan
Doç. Dr. Esin İLHAN
Kırşehir Ahi Evran Üniversitesi, Türkiye
National Yunlin University Of Science And Technology, Tayvan
Yunnan Normal University, Çin
Harran Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | CMES Computer Modeling in Engineering and Sciences (Q2) | ||
| Dergi ISSN | 1526-1492 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2023 |
| Cilt / Sayı / Sayfa | 136 / 1 / 201–221 | DOI | 10.32604/cmes.2023.025094 |
| Makale Linki | https://www.webofscience.com/wos/woscc/full-record/WOS:000950622000009 | ||
| Özet |
| In this study, the design of a computational heuristic based on the nonlinear Lienard model is presented using the efficiency of artificial neural networks (ANNs) along with the hybridization procedures of global and local search approaches. The global search genetic algorithm (GA) and local search sequential quadratic programming scheme (SQPS) are implemented to solve the nonlinear Lienard model. An objective function using the differential model and boundary conditions is designed and optimized by the hybrid computing strength of the GA-SQPS. The motivation of the ANN procedures along with GA-SQPS comes to present reliable, feasible and precise frameworks to tackle stiff and highly nonlinear differential models. The designed procedures of ANNs along with GA-SQPS are applied for three highly nonlinear differential models. The achieved numerical outcomes on multiple trials using the designed procedures are compared to authenticate the correctness, viability and efficacy. Moreover, statistical performances based on different measures are also provided to check the reliability of the ANN along with GA-SQPS. |
| Anahtar Kelimeler |
| Nonlinear Li?nard model | numerical computing | sequential quadratic programming scheme | genetic algorithm | statistical analysis | artificial neural networks |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 12 |
| SCOPUS | 14 |
| Google Scholar | 17 |