research-article

Meta-Heuristic Algorithms-Tuned Elman vs. Jordan Recurrent Neural Networks for Modeling of Electron Beam Welding Process

Authors:

Amit Kumar Das,

Abhishek Rudra Pal,

Sanjib Jaypuria,

Dilip Kumar Pratihar,

Gour Gopal RoyAuthors Info & Claims

Neural Processing Letters, Volume 53, Issue 2

Pages 1647 - 1663

https://doi.org/10.1007/s11063-021-10471-4

Published: 01 April 2021 Publication History

Abstract

A boost in the preference of high energy beam, such as electron beam, laser beam etc. has led to the requirement of its automation through accurate input–output modelling. Modeling of electron beam welding is conducted in the present study through Elman and Jordan recurrent neural networks (RNNs), both having a single feed-back loop, to meet the said requirement. The RNNs are trained using some nature-inspired optimization tools, namely cuckoo search, firefly, flower pollination, and crow search utilizing input–output welding data, obtained from a computational fluid dynamics-based heat transfer and fluid flow welding model. RNN predictions are validated through real experiments. Thus, the effect of change in the position of the feed-back loop on the accuracy of prediction of RNNs is investigated. In addition, a few popular statistical tests have been used to evaluate the performances of the RNNs tuned by various optimization algorithms, where flower pollination-tuned Jordan RNN is observed to yield the best results.

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Cited By

Kaveh MMesgari M(2023)Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive ReviewNeural Processing Letters10.1007/s11063-022-11055-655:4(4519-4622)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1007/s11063-022-11055-6

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Information & Contributors

Information

Published In

cover image Neural Processing Letters

Neural Processing Letters Volume 53, Issue 2

Apr 2021

786 pages

ISSN:1370-4621

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 April 2021

Accepted: 22 February 2021

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Cited By

Kaveh MMesgari M(2023)Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive ReviewNeural Processing Letters10.1007/s11063-022-11055-655:4(4519-4622)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1007/s11063-022-11055-6

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