A novel computing stochastic algorithm to solve the nonlinear singular periodic boundary value problems

In this work, a class of singular periodic nonlinear differential systems (SP-NDS) in nuclear physics is numerically treated by using a novel computing approach based on the Gudermannian neural networks (GNNs) optimized by the mutual strength of global and local search abilities of genetic algorithm...

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Published inInternational journal of computer mathematics Vol. 99; no. 10; pp. 2091 - 2104
Main Authors Sabir, Zulqurnain, Baleanu, Dumitru, Ali, Mohamed R., Sadat, R.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 03.10.2022
Taylor & Francis Ltd
Subjects
Boundary value problems
genetic algorithm
Genetic algorithms
Gudermannian neural networks
Neural networks
Nonlinear systems
Nuclear physics
Periodic singular systems
Quadratic programming
Robustness (mathematics)
sequential quadratic programming
Soft computing
Online AccessGet full text
ISSN0020-7160
1029-0265
DOI10.1080/00207160.2022.2037132

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Abstract In this work, a class of singular periodic nonlinear differential systems (SP-NDS) in nuclear physics is numerically treated by using a novel computing approach based on the Gudermannian neural networks (GNNs) optimized by the mutual strength of global and local search abilities of genetic algorithms (GA) and sequential quadratic programming (SQP), i.e. GNNs-GA-SQP. The stimulation of offering this numerical computing work comes from the aim of introducing a consistent framework that has an effective structure of GNNs optimized with the backgrounds of soft computing to tackle such thought-provoking systems. Two different problems based on the SPNDS in nuclear physics will be examined to check the proficiency, robustness and constancy of the GNNs-GA-SQP. The outcomes obtained through GNNs-GA-SQP are compared with the true results to find the worth of designed procedures based on the multiple trials.
AbstractList In this work, a class of singular periodic nonlinear differential systems (SP-NDS) in nuclear physics is numerically treated by using a novel computing approach based on the Gudermannian neural networks (GNNs) optimized by the mutual strength of global and local search abilities of genetic algorithms (GA) and sequential quadratic programming (SQP), i.e. GNNs-GA-SQP. The stimulation of offering this numerical computing work comes from the aim of introducing a consistent framework that has an effective structure of GNNs optimized with the backgrounds of soft computing to tackle such thought-provoking systems. Two different problems based on the SPNDS in nuclear physics will be examined to check the proficiency, robustness and constancy of the GNNs-GA-SQP. The outcomes obtained through GNNs-GA-SQP are compared with the true results to find the worth of designed procedures based on the multiple trials.
Author Sabir, Zulqurnain
Baleanu, Dumitru
Ali, Mohamed R.
Sadat, R.
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Snippet In this work, a class of singular periodic nonlinear differential systems (SP-NDS) in nuclear physics is numerically treated by using a novel computing...
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SubjectTerms Boundary value problems
genetic algorithm
Genetic algorithms
Gudermannian neural networks
Neural networks
Nonlinear systems
Nuclear physics
Periodic singular systems
Quadratic programming
Robustness (mathematics)
sequential quadratic programming
Soft computing
Title A novel computing stochastic algorithm to solve the nonlinear singular periodic boundary value problems
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