An Inverse recursive algorithm to retrieve the shape of the inaccessible dielectric objects DOI Creative Commons
Ahmet Sefer

An International Journal of Optimization and Control Theories & Applications (IJOCTA), Journal Year: 2024, Volume and Issue: 14(4), P. 378 - 393

Published: Oct. 16, 2024

A regularized electromagnetic iterative inverse algorithm is formulated and implemented to reconstruct the shape of 2D dielectric objects using far-field pattern scattered field data. To achieve this, an integral operator that maps unknown boundary object onto defined solved for boundary. The addressed problem has ill-posed nature inherits nonlinearity. overcome these, proposed solution linearized via Newton by Tikhonov in sense least squares. Besides, dominance shadow region inverse-imaging process exceeded considering superposition multi-incoming plane waves, leading less computational cost a very fast inversion process. Comprehensive numerical analyses are carried out ascertain algorithm's feasibility, revealing it efficient promising.

Language: Английский

An analytical treatment to spatially inhomogeneous population balance model DOI
Saddam Hussain,

Shweta,

Rajesh Kumar

et al.

Chaos Solitons & Fractals, Journal Year: 2024, Volume and Issue: 186, P. 115229 - 115229

Published: July 6, 2024

Language: Английский

Citations

4
Enhancing efficiency in solving coupled Lane–Emden–Fowler equations with a novel Tricomi–Carlitz wavelet method DOI

K. J. Gowtham,

B. J. Gireesha

Zeitschrift für angewandte Mathematik und Physik, Journal Year: 2025, Volume and Issue: 76(2)

Published: Jan. 29, 2025

Language: Английский

Citations

0
Design optimal neural network based on new LM training algorithm for solving 3D - PDEs DOI Creative Commons
Farah Feasal Ghazi, ‎L‎. ‎N‎. ‎M‎. Tawfiq

An International Journal of Optimization and Control Theories & Applications (IJOCTA), Journal Year: 2024, Volume and Issue: 14(3), P. 249 - 260

Published: July 19, 2024

In this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of required high memory, storage and computational overhead because it the updated Hessian approximations in each iteration. suggested implemented to converts original problem into a minimization using feed forward type solve non-linear 3D - PDEs. Also, is obtained by computing parameters learning with highly precise. Examples are provided portray efficiency applicability technique. Comparisons other designs also conducted demonstrate accuracy proposed design.

Language: Английский

Citations

1
Multilayered neural network for power series‐based approximation of fractional delay differential equations DOI
M Suresh Kumar, Sandeep Kumar, Kranti Kumar

et al.

Mathematical Methods in the Applied Sciences, Journal Year: 2024, Volume and Issue: 47(11), P. 8771 - 8785

Published: March 17, 2024

This paper trains a multilayered neural network (MLNN) for solving fractional delay differential equations (FDDEs), including nonlinear and singular types. The proposed methodology involves replacing the unknown functions in with truncated power series expansion. Subsequently, collection of algebraic is solved utilizing an iterative minimization technique that leverages capabilities MLNN architecture. outcomes demonstrate architecture provides required accuracy strong stability compared to several numerical methods.

Language: Английский

Citations

0
Early prediction of fabric quality using machine learning to reduce rework in manufacturing processes DOI Creative Commons
Sema Aydın, Koray Altun

An International Journal of Optimization and Control Theories & Applications (IJOCTA), Journal Year: 2024, Volume and Issue: 14(4), P. 308 - 321

Published: Oct. 9, 2024

The increasing competition and rapid technological advancements in today's business world have raised customer expectations. People now expect quick delivery, low prices, high-quality products. As a result, companies must adapt to this competitive environment survive. Rework, which is significant cost production, increases expenses, reduces production efficiency, can lead attrition. Research shows various efforts across different sectors reduce rework, although there still gap the textile sector's fabric dyeing units. Common problems these units include non-retentive colors, dissatisfaction with shades, repeated due environmental factors or dye vat issues. This study uses logistic regression artificial neural networks models from machine learning predict fabrics will need using data company Bursa. analysis indicates that perform better.

Language: Английский

Citations

0
An Inverse recursive algorithm to retrieve the shape of the inaccessible dielectric objects DOI Creative Commons
Ahmet Sefer

An International Journal of Optimization and Control Theories & Applications (IJOCTA), Journal Year: 2024, Volume and Issue: 14(4), P. 378 - 393

Published: Oct. 16, 2024

A regularized electromagnetic iterative inverse algorithm is formulated and implemented to reconstruct the shape of 2D dielectric objects using far-field pattern scattered field data. To achieve this, an integral operator that maps unknown boundary object onto defined solved for boundary. The addressed problem has ill-posed nature inherits nonlinearity. overcome these, proposed solution linearized via Newton by Tikhonov in sense least squares. Besides, dominance shadow region inverse-imaging process exceeded considering superposition multi-incoming plane waves, leading less computational cost a very fast inversion process. Comprehensive numerical analyses are carried out ascertain algorithm's feasibility, revealing it efficient promising.

Language: Английский

Citations

0