Solving a novel designed second order nonlinear Lane–Emden delay differential model using the heuristic techniques

The aim of the present study is to present a new model based on the nonlinear singular second order delay differential equation of Lane–Emden type and numerically solved by using the heuristic technique. Four different examples are presented based on the designed model and numerically solved by usin...

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Veröffentlicht in:Applied soft computing Jg. 102
Hauptverfasser: Sabir, Zulqurnain, Guirao, Juan L.G., Saeed, Tareq
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.04.2021
Schlagworte:
Artificial neural networks
Delay differential equations
Genetic algorithm
Lane–Emden
Nonlinear
Sequential quadratic programming
Statistical analysis
Nonlinear
Statistical analysis
Sequential quadratic programming
Delay differential equations
Genetic algorithm
Artificial neural networks
Lane–Emden
ISSN:1568-4946, 1872-9681
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Zusammenfassung:The aim of the present study is to present a new model based on the nonlinear singular second order delay differential equation of Lane–Emden type and numerically solved by using the heuristic technique. Four different examples are presented based on the designed model and numerically solved by using artificial neural networks optimized by the global search, local search methods and their hybrid combinations, respectively, named as genetic algorithm (GA), sequential quadratic programming (SQP) and GA-SQP. The numerical results of the designed model are compared for the proposed heuristic technique with the exact/explicit results that demonstrate the performance and correctness. Moreover, statistical investigations/assessments are presented for the accuracy and performance of the designed model implemented with heuristic methodology. •A new model based on the nonlinear singular second order delay differential equation of Lane–Emden type is presented.•The model is solved and numerically by using the heuristic technique.•Four different examples are presented.•Examples are numerically solved by using artificial neural networks optimized.•The numerical results are compared with the heuristic technique.•Statistical investigations are presented for the accuracy of proposed scheme.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2021.107105