Swarming Computational Efficiency to Solve a Novel Third-Order Delay Differential Emden-Fowler System
作者机构：Department of MathematicsFaculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand Department of Mathematics and StatisticsHazara UniversityMansehraPakistan Future Technology Research CenterNational Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlin64002Taiwan Department of Mathematics Statistics and ComputerFaculty of ScienceUbon Ratchathani UniversityUbon Ratchathani34190Thailand
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2022年第73卷第12期
页 面：4833-4849页
核心收录：
学科分类：07[理学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Third-order nonlinear emden-fowler system artificial neural network statistical results particle swarm optimization numerical experimentations local search programming
摘 要：The purpose of this research is to construct an integrated neuro swarming scheme using the procedures of the artificial neural networks(ANNs)with the use of global search particle swarm optimization(PSO)along with the competent local search interior-point programming(IPP)called as ANN-PSOIPP.The proposed computational scheme is implemented for the numerical simulations of the third order nonlinear delay differential Emden-Fowler model(TON-DD-EFM).The TON-DD-EFM is based on two types along with the particulars of shape factor,delayed terms,and singular points.A merit function is performed using the optimization of PSOIPP to find the solutions to the TON-DD-EFM.The effectiveness of the ANN-PSOIPP is certified through the comparison with the exact results for solving four examples of the TON-DD-EFM.The scheme’s efficiency is observed by performing the absolute error in suitable measures found around 10−04 to 10−07.Furthermore,the statistical-based assessments for 100 trials are provided to compute the accuracy,stability,and constancy of the ANNPSOIPP for solving the TON-DD-EFM.
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