Title
Novel design of Morlet wavelet neural network for solving second order Lane-Emden equation
Abstract
In this study, a novel computational paradigm based on Morlet wavelet neural network (MWNN) optimized with integrated strength of genetic algorithm (GAs) and Interior-point algorithm (IPA) is presented for solving second order Lane–Emden equation (LEE). The solution of the LEE is performed by using modelling of the system with MWNNs aided with a hybrid combination of global search of GAs and an efficient local search of IPA. Three variants of the LEE have been numerically evaluated and their comparison with exact solutions demonstrates the correctness of the presented methodology. The statistical analyses are performed to establish the accuracy and convergence via the Theil’s inequality coefficient, mean absolute deviation, and Nash Sutcliffe efficiency based metrics.
Year
DOI
Venue
2020
10.1016/j.matcom.2020.01.005
Mathematics and Computers in Simulation
Keywords
Field
DocType
Lane–Emden equation,Artificial neural networks,Singular,Genetic algorithm,Nonlinear,Interior-point algorithm
Nash–Sutcliffe model efficiency coefficient,Convergence (routing),Applied mathematics,Mathematical optimization,Lane–Emden equation,Correctness,Local search (optimization),Artificial neural network,Genetic algorithm,Morlet wavelet,Mathematics
Journal
Volume
ISSN
Citations 
172
0378-4754
1
PageRank 
References 
Authors
0.35
0
5