Results 101 to 110 of about 6,946,449 (205)

Interpolatory model reduction of dynamical systems with root mean squared error

IFAC-PapersOnLine
The root mean squared error is an important measure used in a variety of applications such as structural dynamics and acoustics to model averaged deviations from standard behavior. For large-scale systems, simulations of this quantity quickly become computationally prohibitive.
Reiter, Sean, Werner, Steffen W. R.
openaire   +2 more sources

Regression coefficients obtained from the negative-binomial regression along with their 95% confidence intervals (in brackets), and root-mean-square prediction error (RMSE) estimated by the use of 10-fold cross-validation.

, 2018
Regression coefficients obtained from the negative-binomial regression along with their 95% confidence intervals (in brackets), and root-mean-square prediction error (RMSE) estimated by the use of 10-fold cross-validation.
Vojtěch Cícha, Michal Bíl, Richard Andrášik (6157583), Vojtěch Cícha (6157589), Jiří Sedoník, Michal Bíl (6157580), Richard Andrášik, Jiří Sedoník (6157586)   +7 more
core   +1 more source

Synthesis of Kantil Tone Using The Frequency Modulation Method

Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Music is a creative form of expression that utilizes sound arranged in specific patterns to create artistic works that are enjoyable to the listener. However, in music, excessive or continuous exposure to loud sounds can damage the hair cells in the ear,
I Ketut Gede Suhartana, Ni Kadek Yulia Dewi, Gst Ayu Vida Mastrika Giri   +2 more
doaj   +1 more source

The relative root mean square error (RRMSE) and relative bias (RBIAS) with varying true density and “random” spatial pattern.

, 2016
The relative root mean square error (RRMSE) and relative bias (RBIAS) with varying true density and “random” spatial pattern.
Md Nabiul Islam Khan (427613), Renske Hijbeek (427612), Uta Berger (204292), Md Asadul Hasan (2851604), Farid Dahdouh-Guebas (427616), Nico Koedam (231281), Uwe Grueters (2851601), S. M. Zahirul Islam (2851607)   +7 more
core   +1 more source

Correspondence On the Selection of Error Measures for Comparisons Among Forecasting Methods [PDF]

Clements and Hendry (1993) proposed the Generalized Forecast Error Second Moment (GFESM) as an improvement to the Mean Square Error in comparing forecasting performance across data series.
JS Armstrong, Robert Fildes
core  

The relative root mean square error (RRMSE) and relative bias (RBIAS) with “natural forests” having different true densities.

, 2016
The relative root mean square error (RRMSE) and relative bias (RBIAS) with “natural forests” having different true densities.
Md Nabiul Islam Khan (427613), Renske Hijbeek (427612), Uta Berger (204292), Md Asadul Hasan (2851604), Farid Dahdouh-Guebas (427616), Nico Koedam (231281), Uwe Grueters (2851601), S. M. Zahirul Islam (2851607)   +7 more
core   +1 more source

Migrative armadillo optimization enabled a one-dimensional quantum convolutional neural network for supply chain demand forecasting.

PLoS ONE
Demand forecasting is a quite challenging task, which is sensitive to several factors such as endogenous and exogenous parameters. In the context of supply chain management, demand forecasting aids to optimize the resources effectively.
Mohamed Irhuma, Ahmad Alzubi, Tolga Öz, Kolawole Iyiola   +3 more
doaj   +1 more source

Reliability with Cronbach alpha intraclass correlation coefficients (ICC) and standard error of the means (SEM), RMS: Root mean square.

, 2019
Reliability with Cronbach alpha intraclass correlation coefficients (ICC) and standard error of the means (SEM), RMS: Root mean square.
Nidhal Zarrouk (6263816), Monoem Haddad (5345357), Karim Chamari (441535), Mohammad Shoaib Prince (6263813), David G. Behm (2549755), Montassar Tabben (6263819)   +5 more
core   +1 more source

Forecasting Maximum Water Level Data for Post Sangkuliman using An Artificial Neural Network Backpropagation Algorithm

JTAM (Jurnal Teori dan Aplikasi Matematika)
Neural Network (NN) is an information processing system that has characteristics similar to biological neural networks. One of the algorithms in NN is Backpropagation Neural Network (BPNN).
Mislan Mislan, Andrea Tri Rian Dani
doaj   +1 more source

Predictive performance of each model based on accuracy, sensitivity, specificity and root mean-square prediction error (RMSPE).

, 2019
Predictive performance of each model based on accuracy, sensitivity, specificity and root mean-square prediction error (RMSPE).
Taylor Gregory (8115224), Miles McBain (8115230), Kerrie Mengersen (177006), James McGree (469641), Samuel Clifford (7139894), Grace Heron (8115218), Erin E. Peterson (2906513), Felipé Gonzalez (8115233), Catherine Leigh (2908049), Ella Wilson (8115221), Ross Brown (1711168), Jacinta Holloway (8115227)   +11 more
core   +1 more source