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

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.

Design of New Reconfigurable Architecture for Implementing a Least Mean Square Finite Impulse Response Filter Using Borrow Select Subtraction (BSLS)

2020 ◽  
Vol 17 (4) ◽  
pp. 1943-1948
Author(s):  
P. Mukunthan ◽  
N. C. Sendhilkumar ◽  
R. Pitchai
Keyword(s):  
Adaptive Filter ◽  
Finite Impulse Response ◽  
Full Adder ◽  
Fir Filter ◽  
Reconfigurable Architecture ◽  
Mean Square ◽  
Least Mean Square ◽  
Lms Filter ◽  
Ripple Carry Adder ◽  
Filter Architecture

A design of reconfigurable architecture of FIR filter has been implemented using a Least Mean Square (LMS) adaptive filter. LMS adaptive filter is mainly sued for reducing the coefficients of the filter. Generally, a LMS filter contains normal adder, subtractor, mixer and a delay part. Most of the concepts deal with an adder namely Full Adder (FA), Ripple Carry Adder (RCA), Carry Select Adder (CSLA), etc., Instead of using CSLA; Borrow Select Subtractor (BSLS) is used in LMS filter architecture. By using BSLA LMS adaptive filter in a reconfigurable FIR filter architecture in the proposed scheme, the area, power and delay will be reduced. The proposed scheme achieves better performance when compared to an existing scheme. The proposed method is implemented in ModelSim tool and efficiency has been calculated by using the device Virtex 6 Low Power in Xilinx ISE Design Suite 12.4.

Improved least mean square adaptive filter algorithm

2013 ◽  
Vol 32 (7) ◽  
pp. 2078-2081
Author(s):  
Cheng-xi WANG ◽  
Yi-an LIU ◽  
Qiang ZHANG
Keyword(s):  
Adaptive Filter ◽  
Mean Square ◽  
Least Mean Square
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