Integrated intelligent computing paradigm for nonlinear multi-singular third-order Emden–Fowler equation

In this study, an advance computational intelligence scheme is designed and implemented to solve third-order nonlinear multiple singular systems represented with Emden–Fowler differential equation (EFDE) by exploiting the efficacy of artificial neural networks (ANNs), genetic algorithms (GAs) and ac...

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Bibliographic Details
Published in:Neural computing & applications 2021-04, Vol.33 (8), p.3417-3436
Main Authors: Sabir, Zulqurnain, Umar, Muhammad, Guirao, Juan L. G., Shoaib, Muhammad, Raja, Muhammad Asif Zahoor
Format: Article
Language:English
Subjects:
Artificial Intelligence
Artificial neural networks
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Differential equations
Genetic algorithms
Image Processing and Computer Vision
Optimization
Original Article
Probability and Statistics in Computer Science
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Summary:In this study, an advance computational intelligence scheme is designed and implemented to solve third-order nonlinear multiple singular systems represented with Emden–Fowler differential equation (EFDE) by exploiting the efficacy of artificial neural networks (ANNs), genetic algorithms (GAs) and active-set algorithm (ASA), i.e., ANN–GA–ASA. In the scheme, ANNs are used to discretize the EFDE for formulation of mean squared error-based fitness function. The optimization task for ANN models of nonlinear multi-singular system is performed by integrated competency GA and ASA. The efficiency of the designed ANN–GA–ASA is examined by solving five different variants of the singular model to check the effectiveness, reliability and significance. The statistical investigations are also performed to authenticate the precision, accuracy and convergence.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-05187-w