.png){kind=icon}
Return
The golden section search algorithm for finding a good shape parameter for meshless collocation methods
.png){kind=icon}
PRE
AI
DOI：10.1016/j.enganabound.2010.03.003.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper we propose to apply the golden section search algorithm to determining a good shape parameter of multiquadrics (MQ) for the solution of partial differential equations. We use two radial basis function based meshless collocation methods, the method of approximate particular solutions (MAPS) and Kansas method, to solve partial differential equations. Due to the severely ill-conditioned matrix system using MQ we also consider the truncated singular value decomposition method (TSVD) to regularize the smoothness of the error versus shape parameter curve so that a reasonably good shape parameter can be identified. We also analyze cost and accuracy for using LU decomposition and TSVD. Numerical results show that the proposed golden section search method is effective and provides a reasonable shape parameter along with acceptable accuracy of the solution. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords:
RBF
Meshless methods
Golden section search
Shape parameter
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.1
Papers:
5.9K
Citations:
9.4K
Organization
Cited Papers
Melatonin Implant Decreases the Density of 2[<sup>125</sup>I]Iodomelatonin Binding Sites in the Chicken Spleen.png)
Neurosignals
NeurosignalsIF0
PREAI
ShareSaveShareSaveShareSave
ShareSaveShareSave
View more