A High-Performance Computing of Internal Rate of Return using a Centroid-based Newton-Raphson Iterative Algorithm

Nagares, Narene M.

doi:10.30534/ijatcse/2020/150922020

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, non-iterative imaging algorithms are mostly used as online imaging algorithms for qualitative analysis or to provide initial values for iterative imaging algorithms. Common iterative algorithms include the gene algorithm (GA) [6], the Landweber algorithm [7], the Newton-Raphson iterative algorithm (NRIA) [8], the Kalman filtering algorithm (KFA) [9], and the extreme learning machine (ELM) algorithm [10]. Compared to non-iterative imaging algorithms, iterative imaging algorithms improve the image reconstruction quality, but this improvement is limited by the high time cost due to many iterations and the difficulty in choosing the parameters of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography

Zhao,

Liu,

Chen

2024

Applied Sciences

View full text Add to dashboard Cite

High-precision images enable electrical capacitance tomography (ECT) to obtain more reliable measurement results, meaning that the reconstruction algorithm is particularly important. Some excellent numerical algorithms have successfully solved the inverse problem for ECT imaging, but their imaging quality is relatively low. To solve this problem, this paper proposes a new reconstruction algorithm based on regularized extreme learning machines (RELMs). The implementation of the algorithm is mainly divided into two steps: (1) according to a large number of training samples, the RELM model can be obtained by the iterative split Bregman (ISB) algorithm, which can describe the mapping relationship between the capacitance correlation coefficient and the imaging target well, and (2) the capacitance correlation coefficient is calculated, which is then used as input to the RELM model to predict the final imaging. Both simulation and experimental results show that the RELM algorithm achieves greater improvement in imaging quality and robustness, and provides new development ideas for the ECT.

show abstract

Section: Introductionmentioning

confidence: 99%