Academia.eduAcademia.edu

Figure 11 – uploaded by Danielle Moreau

See full PDFdownloadDownload figure
Fig 10. Plot of signals for Linear+Nonlinear+Linear primary

Figure 10 Plot of signals for Linear+Nonlinear+Linear primary

Related Figures (13)

Fig. 1 Comparison of local active noise control at (a) a physical sensor; and (b) a virtual sensor [3].
Fig. 1 Comparison of local active noise control at (a) a physical sensor; and (b) a virtual sensor [3].
Fig. 2 Block diagram of FSLMS algorithm using virtual sensing technique
Fig. 2 Block diagram of FSLMS algorithm using virtual sensing technique
Fig. 3 The acoustic duct model used in the simulation
Fig. 3 The acoustic duct model used in the simulation
Fig 4. Schematic of Simulation Experiment in Matlab™ Simulink Fig 5. Primary path as a liner-nonlinear-linear model
Fig 4. Schematic of Simulation Experiment in Matlab™ Simulink Fig 5. Primary path as a liner-nonlinear-linear model
Fig. 7. The last 200 samples of the signal for Wiener (Linear + Nonlinear) primary path case during the training for 30000 samples: é, : error estimate at
Fig. 7. The last 200 samples of the signal for Wiener (Linear + Nonlinear) primary path case during the training for 30000 samples: é, : error estimate at
Fig. 9. The last 200 samples of the signal for Hammerstein (Nonlinear+Linear) primary path case during the training for 30000 samples: é, : error estimate at virtual location, d,, : estimate of desired signal at virtual location, }, : controller output signal estimate at virtual location, e, : received error signal at physical location.
Fig. 9. The last 200 samples of the signal for Hammerstein (Nonlinear+Linear) primary path case during the training for 30000 samples: é, : error estimate at virtual location, d,, : estimate of desired signal at virtual location, }, : controller output signal estimate at virtual location, e, : received error signal at physical location.
Fig. 11. The last 200 samples of the signal for Linear+Nonlinear+Linear primary path case during the training for 30000 samples: e,, : error and (d) error estimate at virtual location, é, .
Fig. 11. The last 200 samples of the signal for Linear+Nonlinear+Linear primary path case during the training for 30000 samples: e,, : error and (d) error estimate at virtual location, é, .
Fig 12. Plot of signals for linear primary path: (a) desired signal at virtual location, d,, , (b) output signal virtual location, 3, , (c) error signal at physical location, e, , and (d) error estimate at virtual location, é,, .
Fig 12. Plot of signals for linear primary path: (a) desired signal at virtual location, d,, , (b) output signal virtual location, 3, , (c) error signal at physical location, e, , and (d) error estimate at virtual location, é,, .
Fig. 13. The last 200 samples of the signal for linear primary path case during
Fig. 13. The last 200 samples of the signal for linear primary path case during
COMPARISON OF NOISE ATTENUATION VALUE Atn(dB) )F FKLMS AND FSLMS BASED ALGORITHMS WITH VIRTUAL SENSING
COMPARISON OF NOISE ATTENUATION VALUE Atn(dB) )F FKLMS AND FSLMS BASED ALGORITHMS WITH VIRTUAL SENSING
Related topics:
AcousticsActive noise control
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved