Neural minimization methods (NMM) for solving variable order fractional delay differential equations (FDDEs) with simulated annealing (SA)

Amber Shaikh; M. Asif Jamal; Fozia Hanif; M. Sadiq Ali Khan; Syed Inayatullah

doi:10.1371/journal.pone.0223476

Loading metrics

Open Access

Peer-reviewed

Research Article

Neural minimization methods (NMM) for solving variable order fractional delay differential equations (FDDEs) with simulated annealing (SA)

Amber Shaikh ,

Roles Data curation, Formal analysis, Methodology, Software, Visualization, Writing – review & editing

* E-mail: amb_shaikh@hotmail.com

Affiliation Department of Humanities and Sciences, National University of Computer and Emerging Sciences, Karachi, Pakistan

http://orcid.org/0000-0003-3289-7679

⨯
M. Asif Jamal,

Roles Conceptualization, Data curation, Resources, Software, Writing – original draft

Affiliation Department of Basic Sciences Federal Urdu University of Art, Science and technology Karachi & Cadet College, Karachi, Pakistan
⨯
Fozia Hanif,

Roles Project administration, Resources, Supervision, Validation, Writing – review & editing

Affiliation Department of Mathematics, University of Karachi, Karachi, Pakistan
⨯
M. Sadiq Ali Khan,

Roles Formal analysis, Methodology, Resources, Software, Writing – review & editing

Affiliation Department of Computer Sciences, University of Karachi, Karachi, Pakistan
⨯
Syed Inayatullah

Roles Formal analysis, Project administration, Resources, Supervision, Writing – review & editing

Affiliation Department of Mathematics, University of Karachi, Karachi, Pakistan
⨯

Neural minimization methods (NMM) for solving variable order fractional delay differential equations (FDDEs) with simulated annealing (SA)

Amber Shaikh,
M. Asif Jamal,
Fozia Hanif,
M. Sadiq Ali Khan,
Syed Inayatullah

Published: October 10, 2019
https://doi.org/10.1371/journal.pone.0223476

Reader Comments

Post a new comment on this article

Note from the PLOS ONE Editors

Posted by PLOS_ONE_Group on 18 Jul 2023 at 17:40 GMT

The concerns raised by reader schris in December 2019 and January 2020 about this article have been evaluated by the journal staff and a member of the journal's editorial board. In light of this assessment the journal determined that the article as published is scientifically sound and meets PLOS ONE’s reporting criteria.

No competing interests declared.

Subject Areas
?

For more information about PLOS Subject Areas, click here.
We want your feedback. Do these Subject Areas make sense for this article? Click the target next to the incorrect Subject Area and let us know. Thanks for your help!

Polynomials
Is the Subject Area "Polynomials" applicable to this article?

Thanks for your feedback.
Differential equations
Is the Subject Area "Differential equations" applicable to this article?

Thanks for your feedback.
Algorithms
Is the Subject Area "Algorithms" applicable to this article?

Thanks for your feedback.
Neural networks
Is the Subject Area "Neural networks" applicable to this article?

Thanks for your feedback.
Simulated annealing
Is the Subject Area "Simulated annealing" applicable to this article?

Thanks for your feedback.
Control theory
Is the Subject Area "Control theory" applicable to this article?

Thanks for your feedback.
Nonlinear systems
Is the Subject Area "Nonlinear systems" applicable to this article?

Thanks for your feedback.
Optimization
Is the Subject Area "Optimization" applicable to this article?

Thanks for your feedback.

Neural minimization methods (NMM) for solving variable order fractional delay differential equations (FDDEs) with simulated annealing (SA)

Neural minimization methods (NMM) for solving variable order fractional delay differential equations (FDDEs) with simulated annealing (SA)

Reader Comments

Post Your Discussion Comment

Why should this posting be reviewed?

Thank You!

Note from the PLOS ONE Editors

Posted by PLOS_ONE_Group on 18 Jul 2023 at 17:40 GMT