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.

scholarly journals Adaptive method to predict and track unknown system behaviors using RLS and LMS algorithms

2021 ◽  
Vol 34 (1) ◽  
pp. 133-140
Author(s):  
Teimour Tajdari
Keyword(s):  
Input Signal ◽  
Output Signal ◽  
Adaptive Filters ◽  
Adaptive Method ◽  
Lms Algorithm ◽  
Recursive Least Squares ◽  
Least Mean Square ◽  
Filter Output ◽  
System Output ◽  
Unknown System

This study investigates the ability of recursive least squares (RLS) and least mean square (LMS) adaptive filtering algorithms to predict and quickly track unknown systems. Tracking unknown system behavior is important if there are other parallel systems that must follow exactly the same behavior at the same time. The adaptive algorithm can correct the filter coefficients according to changes in unknown system parameters to minimize errors between the filter output and the system output for the same input signal. The RLS and LMS algorithms were designed and then examined separately, giving them a similar input signal that was given to the unknown system. The difference between the system output signal and the adaptive filter output signal showed the performance of each filter when identifying an unknown system. The two adaptive filters were able to track the behavior of the system, but each showed certain advantages over the other. The RLS algorithm had the advantage of faster convergence and fewer steady-state errors than the LMS algorithm, but the LMS algorithm had the advantage of less computational complexity.

An Improved ZA-LMS Algorithm for Sparse System Identification with Variable Sparsity

2014 ◽  
Vol 602-605 ◽  
pp. 2415-2419 ◽  
Author(s):  
Hui Luo ◽  
Yun Lin ◽  
Qing Xia
Keyword(s):  
System Identification ◽  
Impulse Response ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Sparse System ◽  
Sparse System Identification ◽  
Least Mean Square Algorithm ◽  
Improved Algorithm

The standard least mean square algorithm does not consider the sparsity of the impulse response,and the performs of the ZA-LMS algorithm deteriorates ,as the degree of system sparsity reduces or non-sparse . Concerning this issue ,the ZA-LMS algorithm is studied and modified in this paper to improve the performance of sparse system identification .The improved algorithm by modify the zero attraction term, which attracts the coefficients only in a certain range (the “inactive” taps), thus have a good performance when the sparsity decreases. The simulations demonstrate that the proposed algorithm significantly outperforms then the ZA-LMS with variable sparisity.

scholarly journals A Dual Adaptive Filter Spike-Based Hardware Architecture for Implementation of a New Active Noise Control Structure

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1945
Author(s):  
Eduardo Pichardo ◽  
Ángel Vázquez ◽  
Esteban R. Anides ◽  
Juan C. Sánchez ◽  
Hector Perez ◽  
...  
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Noise Control ◽  
Active Noise Control ◽  
Computational Cost ◽  
Development Trend ◽  
Mean Square ◽  
Least Mean Square ◽  
Filter Performance ◽  
Active Noise

Presently, the technology development trend of active noise control (ANC) systems is focused on implementing advanced adaptive filters in resource-constrained electronic appliances. Recently, several authors have proved that the use of two adaptive filter algorithms significantly improves the overall adaptive filter performance. However, the computational cost of these approaches is significantly increased since they use two filters simultaneously. Consequently, these filters cannot be implemented in these devices. To solve this problem, we propose a new ANC structure with switching selection based on filtered-x normalized least mean square (FxNLMS) and filtered-x sign least mean square (FxSLMS) algorithms to reduce the computational cost of the ANC system. The improvement of this factor has allowed us to introduce for the first time an advanced spike-based architecture, which can perform dual filter operations using dynamic routing, to be used in real ANC applications. The results have demonstrated that the computational cost of the proposed dual D-FxNLMS/SLMS algorithm is lower compared with previously reported solutions.

scholarly journals Proposed Hybrid Sparse Adaptive Algorithms for System Identification

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Mahmood A. K Abdulsattar ◽  
Samer Hussein Ali
Keyword(s):  
System Identification ◽  
Lms Algorithm ◽  
Complete Analysis ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Least Mean Square ◽  
Sparse System ◽  
Wide Range ◽  
Sparse System Identification ◽  
Zero Attracting

Abstract  For sparse system identification,recent suggested algorithms are  -norm Least Mean Square (  -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named  -ZA-LMS,  -RZA-LMS, p-ZA-LMS and p-RZA-LMS that are designed by merging twoconstraints from previous algorithms to improve theconvergence rate and steady state of MSD for sparse system. In this paper, a complete analysis was done for the theoretical operation of proposed algorithms by exited white Gaussian sequence for input signal. The discussion of mean square deviation (MSD) with regard to parameters of algorithms and system sparsity was observed. In addition, in this paper, the correlation between proposed algorithms and the last recent algorithms were presented and the necessary conditions of these proposed algorithms were planned to improve convergence rate. Finally, the results of simulations are compared with theoretical study (?), which is presented to match closely by a wide-range of parameters.. Keywords: Adaptive filter,  -LMS, zero-attracting, p-LMS, mean square deviation, Sparse system identification.

Bearing Natural Degradation Detection in a Gearbox: A Comparative Study of the Effectiveness of Adaptive Filter Algorithms and Spectral Kurtosis

2014 ◽  
Author(s):  
Faris Elasha ◽  
Cristobal Ruiz-Carcel ◽  
David Mba
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Vibration Signal ◽  
Lms Algorithm ◽  
Complex Signal ◽  
Least Mean Square ◽  
Bearing Faults ◽  
Spectral Kurtosis ◽  
Bearing Defects ◽  
Filter Algorithms

Bearing faults detection at the earliest stages is vital in avoiding future catastrophic failures. Many traditional techniques have been established and utilized in detecting bearing faults, though, these diagnostic techniques are not always successful when the bearing faults take place in gearboxes where the vibration signal is complex; under such circumstances it may be necessary to separate the bearing signal from the complex signal. The objective of this paper is to assess the effectiveness of an adaptive filter algorithms compared to a Spectral Kurtosis (SK) algorithm in diagnosing a bearing defects in a gearbox. Two adaptive filters have been used for the purpose of bearing signal separation, these algorithms were Least Mean Square (LMS) and Fast Block LMS (FBLMS) algorithms. These algorithms were applied to identify a bearing defects in a gearbox employed for an aircraft control system for which endurance tests were performed. The results show that the LMS algorithm is capable of detecting the bearing fault earlier in comparison to the other algorithms.

scholarly journals A Robust Adaptive Filter for a Complex Hammerstein System

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 162 ◽  
Author(s):  
Guobing Qian ◽  
Dan Luo ◽  
Shiyuan Wang
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Impulsive Noise ◽  
Complex Domain ◽  
Mean Square ◽  
Least Mean Square ◽  
Hammerstein System ◽  
Robust Adaptive ◽  
The Stability ◽  
Maximum Correntropy Criterion

The Hammerstein adaptive filter using maximum correntropy criterion (MCC) has been shown to be more robust to outliers than the ones using the traditional mean square error (MSE) criterion. As there is no report on the robust Hammerstein adaptive filters in the complex domain, in this paper, we develop the robust Hammerstein adaptive filter under MCC to the complex domain, and propose the Hammerstein maximum complex correntropy criterion (HMCCC) algorithm. Thus, the new Hammerstein adaptive filter can be used to directly handle the complex-valued data. Additionally, we analyze the stability and steady-state mean square performance of HMCCC. Simulations illustrate that the proposed HMCCC algorithm is convergent in the impulsive noise environment, and achieves a higher accuracy and faster convergence speed than the Hammerstein complex least mean square (HCLMS) algorithm.

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

scholarly journals An Effective Adaptive Combination Strategy for Distributed Learning Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5723
Author(s):  
Chundong Xu ◽  
Qinglin Li ◽  
Dongwen Ying
Keyword(s):  
Minimum Variance ◽  
Iteration Scheme ◽  
Estimation Problem ◽  
Lms Algorithm ◽  
Unbiased Estimation ◽  
Mean Square ◽  
Least Mean Square ◽  
Combination Strategy ◽  
Adaptive Combination ◽  
Diffusion Networks

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.

Sign in / Sign up

Export Citation Format

Share Document