| Yazarlar |
Li Yan
|
|
Zulqurnain Sabir
|
Doç. Dr. Esin İLHAN
Kırşehir Ahi Evran Üniversitesi |
|
Muhammad Asif Zahoor Raja
|
|
Wei Gao
|
|
Haci Mehmet Baskonus
|
| 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 |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES |
| Dergi ISSN | 1526-1492 |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | Türkçe |
| Basım Tarihi | 01-2023 |
| Cilt No | 136 |
| Sayı | 1 |
| Sayfalar | 201 / 221 |
| Doi Numarası | 10.32604/cmes.2023.025094 |
| Makale Linki | https://www.webofscience.com/wos/woscc/full-record/WOS:000950622000009 |
| Atıf Sayıları | |
| WoS | 12 |
| Google Scholar | 14 |
