Error correction for phase detection by recursive algorithm real time DFT

2002 ◽  
Vol 141 (1) ◽  
pp. 8-17 ◽  
Author(s):  
Tsuyoshi Funaki ◽  
Kenji Matsuura ◽  
Shunsuke Tanaka
Keyword(s):  
Error Correction ◽  
Real Time ◽  
Recursive Algorithm ◽  
Phase Detection

scholarly journals Error Correction for Phase Detection by Recursive Algorithm Real Time DFT

2001 ◽  
Vol 121 (9) ◽  
pp. 1085-1093 ◽  
Author(s):  
Tsuyoshi Funaki ◽  
Kenji Matsuura ◽  
Shunsuke Tanaka
Keyword(s):  
Error Correction ◽  
Real Time ◽  
Recursive Algorithm ◽  
Phase Detection

scholarly journals A Kinematic Error Controller for Real-Time Kinematic Error Correction of Industrial Robots

2021 ◽  
Vol 53 ◽  
pp. 705-715
Author(s):  
Mitchell R. Woodside ◽  
Joseph Fischer ◽  
Patrick Bazzoli ◽  
Douglas A. Bristow ◽  
Robert G. Landers
Keyword(s):  
Error Correction ◽  
Real Time ◽  
Industrial Robots ◽  
Kinematic Error ◽  
Real Time Kinematic

Phase error correction in real time Fourier transform spectroscopy

1976 ◽  
Vol 16 (3) ◽  
pp. 345-348 ◽  
Author(s):  
Ph. Marteau ◽  
J. Obriot ◽  
C. Chalier ◽  
G.H. Arie
Keyword(s):  
Fourier Transform ◽  
Error Correction ◽  
Real Time ◽  
Phase Error ◽  
Fourier Transform Spectroscopy

scholarly journals Stochastic model specification in Markov switching vector error correction models

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Niko Hauzenberger ◽  
Florian Huber ◽  
Michael Pfarrhofer ◽  
Thomas O. Zörner
Keyword(s):  
Stochastic Model ◽  
Error Correction ◽  
Real Time ◽  
Euro Area ◽  
Model Specification ◽  
Vector Error Correction ◽  
Vector Error ◽  
Common Distribution ◽  
Vector Error Correction Models ◽  
Shrinkage Priors

AbstractThis paper proposes a hierarchical modeling approach to perform stochastic model specification in Markov switching vector error correction models. We assume that a common distribution gives rise to the regime-specific regression coefficients. The mean as well as the variances of this distribution are treated as fully stochastic and suitable shrinkage priors are used. These shrinkage priors enable to assess which coefficients differ across regimes in a flexible manner. In the case of similar coefficients, our model pushes the respective regions of the parameter space towards the common distribution. This allows for selecting a parsimonious model while still maintaining sufficient flexibility to control for sudden shifts in the parameters, if necessary. We apply our modeling approach to real-time Euro area data and assume transition probabilities between expansionary and recessionary regimes to be driven by the cointegration errors. The results suggest that the regime allocation is governed by a subset of short-run adjustment coefficients and regime-specific variance-covariance matrices. These findings are complemented by an out-of-sample forecast exercise, illustrating the advantages of the model for predicting Euro area inflation in real time.

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