AN EFFICIENT IMPLEMENTATION OF LEAST MEAN SQUARE ADAPTIVE FIR FILTER BASED ON DISTRIBUTED ARITHMETIC

2019 ◽  
Vol 19 (1) ◽  
pp. 9-32
Author(s):  
D. Kalaiyarasi ◽  
T. Kalpalatha Reddy
Keyword(s):  
Fir Filter ◽  
Efficient Implementation ◽  
Mean Square ◽  
Least Mean Square ◽  
Distributed Arithmetic ◽  
Adaptive Fir

Robust least mean square adaptive FIR filter algorithm

2001 ◽  
Vol 148 (5) ◽  
pp. 332 ◽  
Author(s):  
Z. Banjac ◽  
B. Kovačević ◽  
M. Veinović ◽  
M. Milosavljević
Keyword(s):  
Fir Filter ◽  
Mean Square ◽  
Least Mean Square ◽  
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

scholarly journals FPGA based least mean square algorithm for noise cancellation in communication system

2018 ◽  
Vol 7 (3.3) ◽  
pp. 165
Author(s):  
Praveen Reddy ◽  
Dr Baswaraj Gadgay
Keyword(s):  
Adaptive Filter ◽  
Communication System ◽  
Noise Cancellation ◽  
Mean Square ◽  
Least Mean Square ◽  
Distributed Arithmetic ◽  
Negative Effect ◽  
Least Mean Square Algorithm ◽  
Design Comparison

We present modified Distributed Arithmetic (DA) based architecture for LMS Adaptive filter which has improved the throughput of the filter also area and power has been comparatively been reduced. As we know, the adaptive filter uses continuous recalculation and generation of new coefficients will generate the negative effect on the use of algorithm. We have used a special temporary LUT addressing technique has overcome the issues resulting in better performance and good results. In this paper, we have discussed about the adaptive filter and implementation of DA adaptive filter and also discussed the results obtained from the design. Comparison with traditional de-sign has also been done to show the effectiveness of the algorithm.   

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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