Zobrazeno 1 - 10
of 1 194
pro vyhledávání: '"LEVENBERG-MARQUARDT METHOD"'
Publikováno v:
AIMS Mathematics, Vol 9, Iss 9, Pp 24610-24635 (2024)
In this paper, aiming at the nonlinear equations, a new two-step Levenberg–Marquardt method was proposed. We presented a new Levenberg–Marquardt parameter to obtain the trial step. A new modified Metropolis criterion was used to adjust the upper
Externí odkaz:
https://doaj.org/article/61e3be6c6bc44ea38eeed51b8d06604c
Publikováno v:
Chinese Journal of Magnetic Resonance, Vol 41, Iss 2, Pp 128-138 (2024)
In recent years, Halbach magnet has been extensively used in miniaturized NMR spectrometers. However, the inhomogeneity of the magnetic field of permanent magnets poses a challenge to passive shimming method. In this paper, we conducted a passive shi
Externí odkaz:
https://doaj.org/article/9c909fe4039a4b8fbcfc3d3022201575
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 2, Pp 3057-3085 (2024)
Abstract This paper introduces a novel approach aimed at enhancing the control performance of a specific class of unknown multiple-input and multiple-output nonlinear systems. The proposed method involves the utilization of a fractional-order fuzzy s
Externí odkaz:
https://doaj.org/article/27f680b146a04330914f7ab31b4daa35
Autor:
J.G. AL-Juaid, Zeeshan Khan, Aatif Ali, Muhammad Bilal Riaz, Taseer Muhammad, Jana Shafi, Saeed Islam
Publikováno v:
Case Studies in Thermal Engineering, Vol 56, Iss , Pp 104208- (2024)
The present study examines the impact of incorporating soft computing algorithms during neural network training on the evaluation and prediction performance of artificial neural networks. The research centers on a magneto hydrodynamic flow model (RHM
Externí odkaz:
https://doaj.org/article/94af6b34606b43118177677723d4a68c
Publikováno v:
International Journal of Numerical Methods for Heat & Fluid Flow, 2023, Vol. 33, Issue 8, pp. 3025-3055.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/HFF-12-2022-0720
Autor:
Luyao Zhao, Jingyong Tang
Publikováno v:
AIMS Mathematics, Vol 8, Iss 8, Pp 18649-18664 (2023)
We present a family of inexact Levenberg-Marquardt (LM) methods for the nonlinear equations which takes more general LM parameters and perturbation vectors. We derive an explicit formula of the convergence order of these inexact LM methods under the
Externí odkaz:
https://doaj.org/article/380057d67739433181ad45cf19d4e7b1
Autor:
Saddiqa Hussain, Saeed Islam, Kottakkaran Sooppy Nisar, Muhammad Asif Zahoor Raja, Muhammad Shoaib, Mohamed Abbas, C Ahamed Saleel
Publikováno v:
Heliyon, Vol 9, Iss 12, Pp e22765- (2023)
Applications of artificial intelligence (AI) via soft computing procedures have attracted the attention of researchers due to their effective modeling, simulation procedures, and detailed analysis. In this article, the designing of intelligence compu
Externí odkaz:
https://doaj.org/article/669ef94550ca438a8f8c9fec3b7bb24d
Autor:
Yang Han, Shaoping Rui
Publikováno v:
Symmetry, Vol 16, Iss 6, p 674 (2024)
The Levenberg–Marquardt (LM) method is one of the most significant methods for solving nonlinear equations as well as symmetric and asymmetric linear equations. To improve the method, this paper proposes a new adaptive LM algorithm by modifying the
Externí odkaz:
https://doaj.org/article/cb79b04a691c4f088da6b335afca2a61
Publikováno v:
Open Mathematics, Vol 20, Iss 1, Pp 998-1012 (2022)
In this article, we analyze the convergence rate of the modified Levenberg-Marquardt (MLM) method under the Hölderian local error bound condition and the Hölderian continuity of the Jacobian, which are more general than the local error bound condit
Externí odkaz:
https://doaj.org/article/2a745e4b469746beae2385c0b84bd2b7