Results 241 to 250 of about 145,155 (289)

Continuous blood glucose monitoring prediction for diabetes using evolving neural network. [PDF]

Sci Rep
Vaughan N.
europepmc   +1 more source

The peak shifting electricity consumption management and influencing factors of smart grid from recurrent neural network model and deep learning. [PDF]

Sci Rep
Wang F, Huang D, Lu W.
europepmc   +1 more source

FPGA-Parallelized Digital Filtering for Real-Time Linear Envelope Detection of Surface Electromyography Signal on cRIO Embedded System. [PDF]

Sensors (Basel)
Achmamad A, Jbari A, Yaakoubi N.
europepmc   +1 more source

Temperature trend prediction with explainable artificial intelligence and PCA based machine learning: a case study of Zonguldak, Turkey. [PDF]

Sci Rep
Arslan RU, Aksoy B, Yapıcı İŞ.
europepmc   +1 more source

Analysis of Mean-Square-Error (MSE) for fixed-point FFT units

2011 IEEE International Symposium of Circuits and Systems (ISCAS), 2011
Range and precision analysis are important steps in assigning suitable integer and fractional bit-widths to the fixed-point variables in a design such that no overflow occurs and a given error bound on maximum mismatch and (or) Mean-Square-Error (MSE) is satisfied.
O. Sarbishei, K. Radecka
openaire   +2 more sources

MSE < Variance? A pitfall in calculating the mean square error

Model Assisted Statistics and Applications, 2011
When calculating the mean square error (MSE), it is possible to encounter a situation where the variance of a parameter of interest is larger than its mean square error. In theory, this is impossible because MSE is the sum of variance and bias squared; even when bias is zero, the MSE should be equal to, and not less than, the variance.
Edmond Siu-Woon Ng
openaire   +2 more sources

An MSE comparison of the restricted Stein-rule and minimum mean squared error estimators in regression

Test, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kazuhiro Ohtani
openaire   +2 more sources

The Mean Square Error (MSE) Performance Criteria

, 1986
Adaptive signal processing algorithms generally attempt to optimize a performance measure that is a function of the unknown parameters to be identified. The most pervasive of these performance measures are based upon squared prediction errors, although the specific prediction error used in adaptation often depends upon the particular algorithm.
S.T. Alexander
openaire   +2 more sources

Multiresponse robust design: Mean square error (MSE) criterion

Applied Mathematics and Computation, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Onur Köksoy
openaire   +3 more sources