Adaptive FIR Filter Algorithms

1988 ◽  
pp. 15-57
Author(s):  
Bernard Mulgrew ◽  
Colin F. N. Cowan
Keyword(s):  
Fir Filter ◽  
Filter Algorithms ◽  
Adaptive Fir

A cascadable adaptive FIR filter VLSI IC

2005 ◽  
Author(s):  
D. Borth ◽  
I. Gerson ◽  
J. Haug
Keyword(s):  
Fir Filter ◽  
Adaptive Fir

Adaptive FIR Filter to Compensate for Speaker Non-linearity

2014 ◽  
pp. 301-309
Author(s):  
Varsha Varadarajan ◽  
Kinnera Pallavi ◽  
Gautam Balgovind ◽  
J. Selvakumar
Keyword(s):  
Fir Filter ◽  
Adaptive Fir

Low power area efficient adaptive FIR filter for hearing aids using distributed arithmetic architecture

2020 ◽  
Vol 23 (2) ◽  
pp. 287-296 ◽  
Author(s):  
P. V. Praveen Sundar ◽  
D. Ranjith ◽  
T. Karthikeyan ◽  
V. Vinoth Kumar ◽  
Balajee Jeyakumar
Keyword(s):  
Low Power ◽  
Hearing Aids ◽  
Fir Filter ◽  
Distributed Arithmetic ◽  
Adaptive Fir ◽  
Area Efficient

Adaptive FIR filter for frequency and power estimation of sinusoids

2019 ◽  
Vol 11 (1) ◽  
pp. 22 ◽  
Author(s):  
Fadia B. Elissa ◽  
Mohammad J. Mismar
Keyword(s):  
Power Estimation ◽  
Fir Filter ◽  
Adaptive Fir

scholarly journals A Model Predictive Algorithm for Active Control of Nonlinear Noise Processes

2005 ◽  
Vol 12 (3) ◽  
pp. 227-237 ◽  
Author(s):  
Qi-Zhi Zhang ◽  
Woon-Seng Gan ◽  
Ya-li Zhou
Keyword(s):  
Time Delay ◽  
Active Noise Control ◽  
Weight Vector ◽  
Fir Filter ◽  
Secondary Source ◽  
Least Mean Square ◽  
Predictive Algorithm ◽  
Primary Path ◽  
Fxlms Algorithm ◽  
Adaptive Fir

In this paper, an improved nonlinear Active Noise Control (ANC) system is achieved by introducing an appropriate secondary source. For ANC system to be successfully implemented, the nonlinearity of the primary path and time delay of the secondary path must be overcome. A nonlinear Model Predictive Control (MPC) strategy is introduced to deal with the time delay in the secondary path and the nonlinearity in the primary path of the ANC system. An overall online modeling technique is utilized for online secondary path and primary path estimation. The secondary path is estimated using an adaptive FIR filter, and the primary path is estimated using a Neural Network (NN). The two models are connected in parallel with the two paths. In this system, the mutual disturbances between the operation of the nonlinear ANC controller and modeling of the secondary can be greatly reduced. The coefficients of the adaptive FIR filter and weight vector of NN are adjusted online. Computer simulations are carried out to compare the proposed nonlinear MPC method with the nonlinear Filter-x Least Mean Square (FXLMS) algorithm. The results showed that the convergence speed of the proposed nonlinear MPC algorithm is faster than that of nonlinear FXLMS algorithm. For testing the robust performance of the proposed nonlinear ANC system, the sudden changes in the secondary path and primary path of the ANC system are considered. Results indicated that the proposed nonlinear ANC system can rapidly track the sudden changes in the acoustic paths of the nonlinear ANC system, and ensure the adaptive algorithm stable when the nonlinear ANC system is time variable.

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