Improved hybrid variable and fixed step size least mean square adaptive filter algorithm with application to time varying system identification

2015 ◽  
Author(s):  
Farqad Y. Farhan ◽  
Siddeeq Y. Ameen
Keyword(s):  
System Identification ◽  
Adaptive Filter ◽  
Time Varying ◽  
Mean Square ◽  
Least Mean Square ◽  
Step Size ◽  
Hybrid Variable

A time-varying signal processing method for Coriolis mass flowmeter based on adaptive filter

2016 ◽  
Vol 40 (1) ◽  
pp. 261-268 ◽  
Author(s):  
Feng Dan ◽  
Fan Shangchun ◽  
Zheng Dezhi
Keyword(s):  
Signal Processing ◽  
Adaptive Filter ◽  
Processing Method ◽  
Time Varying ◽  
Mean Square ◽  
Least Mean Square ◽  
Mass Flowmeter ◽  
Signal Processing Method ◽  
Coriolis Mass Flowmeter ◽  
Nlms Algorithm

In this paper, the normalized least mean square (NLMS) algorithm, a time-varying signal processing method, is employed in a Coriolis mass flowmeter (CFM) to improve its weak anti-jamming capability. Initially, the fundamental principles of the NLMS algorithm adopted in the adaptive filter are analysed. Then, the NLMS algorithm is applied to analyse the signal processing of the CFM at different flow rates in experiments. By comparing several performance indicators and spectrum diagrams from being filtered by the NLMS algorithm and the least mean square (LMS) algorithm, the results indicate that the NLMS algorithm can lead to a better anti-jamming capability and reduce the influence of noise efficiently for the CFM. In addition, the NLMS method has a faster convergence speed and fewer stable errors than the LMS method. Therefore, the NLMS can improve the quality of the output signal of the CFM.

scholarly journals System Identification using Adaptive Filters

2020 ◽  
Vol 9 (9) ◽  
pp. 409-414
Keyword(s):  
System Identification ◽  
Adaptive Filter ◽  
Adaptive Filters ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Iir Filter ◽  
Unknown System

Identification of system is one of the major applications of an adaptive filters, mainly Least Mean Square (LMS) algorithm, because of its ease in calculations, the ability to withstand or overcome any conditions. The unknown System can be a FIR or an IIR filter. Same input is fed into both undefined system (which is unknown to us) and the adaptive filter, their outputs will be subtracted and the error subtracted signal will be given to adaptive filter. The adaptive filter is modified until the system which is unknown and the adaptive filter becomes relatively equal. System identification defines the type and functionality of the system. By utilizing the weights, the output of the system for any input can be predicted.

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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