2009 52nd IEEE International Midwest Symposium on Circuits and Systems 2009
DOI: 10.1109/mwscas.2009.5235957
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Description of adaptive fuzzy filtering using the DSP TMS320C6713

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Cited by 4 publications
(2 citation statements)
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“…However, after conducting a study of the local and global convergence of τ c,k in [16], it was concluded that it can be considered a stochastic system with correlated disturbances, and due to this, it is possible to use an auto-regressive moving average model ARMA (1,1) for the mathematical representation of the execution times τ c,k . The system parameters can be obtained by using a digital filter based on the the Instrumental Variable Method IVM [16,18,19] and fuzzy filtering [17,20,21].…”
Section: Model For Reconstruction Of the Communication Times In A Tel...mentioning
confidence: 99%
See 1 more Smart Citation
“…However, after conducting a study of the local and global convergence of τ c,k in [16], it was concluded that it can be considered a stochastic system with correlated disturbances, and due to this, it is possible to use an auto-regressive moving average model ARMA (1,1) for the mathematical representation of the execution times τ c,k . The system parameters can be obtained by using a digital filter based on the the Instrumental Variable Method IVM [16,18,19] and fuzzy filtering [17,20,21].…”
Section: Model For Reconstruction Of the Communication Times In A Tel...mentioning
confidence: 99%
“…However, it is also possible to find execution times τ c,k reconstruction models, which try to obtain τc,k at each time instance. These models are based on the auto-regressive moving averages model (ARMA) in which parameters that identify the system were calculated using digital filtering [16][17][18][19][20][21].…”
Section: Introductionmentioning
confidence: 99%