Results 271 to 280 of about 1,334,722 (325)

Reduction of root-mean-square error in faceted space antennas

AIAA Journal, 1984
This paper examines the potential for reducing root-mean-square surface errors in shallow faceted reflectors by replacing flat facets with laterally curved membrane facets. Exact solutions are obtained for the small lateral deflections of equilateral triangular and rectangular membranes subject to isotropic tension and parabolic edge deflections. These
W. Fichter
semanticscholar   +3 more sources

Accuracy Analysis of Anisotropic Yield Functions based on the Root‐Mean Square Error

AIP Conference Proceedings, 2010
This paper evaluates the accuracy of popular anisotropic yield functions based on the root‐mean square error (RMSE) of the yield stresses and the R‐values. The yield functions include Hill48, Yld89, Yld91, Yld96, Yld2000‐2d, BBC2000 and Yld2000‐18p yield criteria.
H. Huh, Y. Lou, G. Bae, Changsoo Lee
semanticscholar   +2 more sources

Root-Mean-Square Error

ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2017
Jonathan Nagler
semanticscholar   +2 more sources

Bayesian Estimation of the Root Mean Square Error of Approximation

Proceedings of the 2024 AERA Annual Meeting
James Uanhoro
semanticscholar   +2 more sources

Low-Complexity Double-Vector Model Predictive Control With Minimum Root Mean Square Error for Three-Phase Three-Level Inverters

IEEE Journal of Emerging and Selected Topics in Power Electronics, 2023
The conventional finite-control-set model predictive control (FCS-MPC) approach for controlling three-phase three-level inverters is plagued by significant current ripple. To minimize the root mean square error (RMSE) of the current control, this article
Hanbin Zhou, Liansheng Huang, Jian Yang, Jingyang Zhou, Dongran Song, Xiaojiao Chen, M. Dong   +6 more
semanticscholar   +1 more source

Generative adversarial network (GAN) and enhanced root mean square error (ERMSE): deep learning for stock price movement prediction

Multimedia tools and applications, 2021
The prediction of stock price movement direction is significant in financial circles and academic. Stock price contains complex, incomplete, and fuzzy information which makes it an extremely difficult task to predict its development trend. Predicting and
Ashish Kumar, A. Alsadoon, P. Prasad, S. Abdullah, Tarik A. Rashid, Duong Thu Hang Pham, T. Nguyen   +6 more
semanticscholar   +1 more source

Effects of Multivariate Non-Normality and Missing Data on the Root Mean Square Error of Approximation

Structural Equation Modeling: A Multidisciplinary Journal, 2021
The root mean square error of approximation (RMSEA) with various corrections for non-normality is a common fit index in structural equation modeling (SEM).
Lisa J. Jobst, C. Heine, Max Auerswald, Morten Moshagen   +3 more
semanticscholar   +1 more source

On the root mean square error (RMSE) calculation for parameter estimation of photovoltaic models: A novel exact analytical solution based on Lambert W function

, 2020
In the literature, one can find a lot of methods and techniques employed to estimate single diode solar photovoltaic (PV) cell parameters. The efficiency of these methods is usually tested by calculating the Root Mean Square Error (RMSE) between the ...
Martin P. Ćalasan, S. Aleem, A. Zobaa
semanticscholar   +1 more source

The Wiener (Root Mean Square) Error Criterion in Filter Design and Prediction

Journal of Mathematics and Physics, 1946
N. Levinson
semanticscholar   +2 more sources

Estimating the Maximum Likelihood Root Mean Square Error of Approximation (RMSEA) with Non-normal Data: A Monte-Carlo Study

Structural Equation Modeling: A Multidisciplinary Journal, 2020
Recent research has provided formulae for estimating the maximum likelihood (ML) RMSEA when mean or mean and variance, corrections for non-normality are applied to the likelihood ratio test statistic.
Chuanji Gao, Dexin Shi, Albert Maydeu-Olivares   +2 more
semanticscholar   +1 more source