Results 41 to 50 of about 403,368 (289)

Oil price forecasting using gene expression programming and artificial neural networks [PDF]

, 2016
This study aims to forecast oil prices using evolutionary techniques such as gene expression programming (GEP) and artificial neural network (NN) models to predict oil prices over the period from January 2, 1986 to June 12, 2012.
El-Masry, AA, Mostafa, M
core   +2 more sources

A Comparison of Root Mean Square Errors on Skeletonization Methods [PDF]

International Journal of Applied Physics and Mathematics, 2012
Vectorization is the most fundamental operation in interpretation of line drawings and document analysis. There are several reasons for converting image vectorization. Vector data is normally created from existing natural source image like photographs, scanned images.
Neeti Daryal, Vinod Kumar
openaire   +1 more source

A novel modification to backpropagation sample selection strategy [PDF]

, 1997
Random sample selection method in backpropagation results in convergence on the error (root of mean squared error, RMSE) surface. These problems, which are caused by the extreme (worst-case) errors, can be solved by a different sample selection strategy.
Redei, Laszlo, Wallinga, Hans
core   +3 more sources

Improving Accuracy of Solar Cells Parameters Extraction by Minimum Root Mean Square Error [PDF]

2020 55th International Universities Power Engineering Conference (UPEC), 2020
This paper presents a technique for enhancing the accuracy of parameters extraction of photovoltaic (PV) cells from experimental current-voltage (I-V) curve. This technique is based on entering nearly all the possible points of an I-V curve to extract the slopes near the open circuit voltage and short circuit current to determine approximate values of ...
Atia, Abdulhamid, Anayi, Fatih, Min, Gao
openaire   +1 more source

Relationshio between Root Mean Square Error and Probable Error.

Journal of the Japan society of photogrammetry and remote sensing, 1994
The relationship between Root Mean Square (RMS) error and probable error was investigated under the condition of normal distribution with independent x, y and z error components. In the ideal case of no bias and equal standard deviations (in the case of two or three dimensions), the ratios of 90% probable error to RMS error are 1.645 (one dimensional ...
Ryutaro TATEISHI, Chengang WEN
openaire   +2 more sources

Hybrid ARIMA-LSTM for COVID-19 forecasting: a comparative AI modeling study [PDF]

PeerJ Computer Science
Pandemics present critical challenges to global health systems, economies, and societal structures, necessitating the development of accurate forecasting models for effective intervention and resource allocation.
Al Mahmud, Syed Husni Noor Syed Hatim Noor, Kamarul Imran Musa, Firdaus Mohamad Hamzah, Zainab Mat Yudin, Noorshaida Kamaruddin, Ashwini M. Madawana, Mohamad Arif Awang Nawi   +7 more
doaj   +2 more sources

Optimal interpolation of satellite and ground data for irradiance nowcasting at city scales [PDF]

, 2017
We use a Bayesian method, optimal interpolation, to improve satellite derived irradiance estimates at city-scales using ground sensor data. Optimal interpolation requires error covariances in the satellite estimates and ground data, which define how ...
Alexander D. Cronin, Antonio T. Lorenzo, Drews, D’Agostino, Escrig, Frei, Ghonima, Journée, Kalnay, Kim, Kühnert, Low, Matthias Morzfeld, Mieslinger, Perez, Polo, Polo, Rasmussen, Reno, Ruiz-Arias, Saint-Drenan, Schumann, Vicente, Vignola, William F. Holmgren   +24 more
core   +1 more source

A weighted error-minimizer parameter estimation technique for one-inflated positive Poisson distribution

Results in Control and Optimization
An error-minimizing estimator is always preferred in model fittings. However, each error-minimizing estimator minimizes error differently. This paper combines four error-minimizing estimators, which are root mean-squared error, mean absolute error, root ...
Razik Ridzuan Mohd Tajuddin
doaj   +1 more source

Using intelligent optimization methods to improve the group method of data handling in time series prediction [PDF]

, 2008
In this paper we show how the performance of the basic algorithm of the Group Method of Data Handling (GMDH) can be improved using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).
A. Episcopos, A.G. Ivakhnenko, D. Chappell, D.E. Goldberg, I. Hayashi, J.H. Holland, J.R. Koza, S.A. Billings   +7 more
core   +1 more source

Soundness and completeness of quantum root-mean-square errors [PDF]

npj Quantum Information, 2019
AbstractDefining and measuring the error of a measurement is one of the most fundamental activities in experimental science. However, quantum theory shows a peculiar difficulty in extending the classical notion of root-mean-square (rms) error to quantum measurements.
openaire   +2 more sources