An improverd variable step size LMS adaptive filtering algorithm
0
Citation
7
Reference
10
Related Paper
Abstract:
LMS (Least Mean Square) algorithm is widely used due to its simple and stable performance. As is well known, there is an inherent conflict between the convergence rate and stead-state misadjustment, which can be overcome through the adjustment of size factor. The paper has analyzed some LMS algorithms that already existed and a new improved variable step-size LMS algorithm is presented. The computer simulation results are consistent with the theoretic analysis, ?which show that the algorithm not only has a faster convergence rate, but also has a smaller steady-state error.In order to achieve the optimum filtering effect,it makes the adaptive filter adjust its units impulse response characteristics automatically on the working environment changed.This paper presents a kind of adaptive algorithm: Least Mean Square(LMS algorithm).As the algorithm is realized simply and has stability with respect to the change of signal statistical characteristics,LMS algorithm is used widely.According to disadvantages of adaptive filter to realize LMS using hardware adaptive filter is simulated which is based on LMS algorithm with Matlab.Results of simulation show that this kind of adaptive filter not only can filter the signal noise,but also recognize the system.
Adaptive algorithm
Cite
Citations (1)
In this paper a modification of the Least Mean Square (LMS) adaptive algorithm is presented based on a modification of the codification of the error signal. This modification allows having an algorithm easier to implement in a digital adaptive filter, besides that a greater convergence speed is obtained. A comparison of the proposed modification with the conventional LMS algorithm and some of its known variants is provided to show the advantages of proposed algorithm.
Adaptive algorithm
Cite
Citations (11)
Employing a recently introduced unified adaptive filter theory, we show how the performance of a large number of important adaptive filter algorithms can be predicted within a general framework in nonstationary environment. This approach is based on energy conservation arguments and does not need to assume a Gaussian or white distribution for the regressors. This general performance analysis can be used to evaluate the mean square performance of the Least Mean Square (LMS) algorithm, its normalized version (NLMS), the family of Affine Projection Algorithms (APA), the Recursive Least Squares (RLS), the Data-Reusing LMS (DR-LMS), its normalized version (NDR-LMS), the Block Least Mean Squares (BLMS), the Block Normalized LMS (BNLMS), the Transform Domain Adaptive Filters (TDAF) and the Subband Adaptive Filters (SAF) in nonstationary environment. Also, we establish the general expressions for the steady-state excess mean square in this environment for all these adaptive algorithms. Finally, we demonstrate through simulations that these results are useful in predicting the adaptive filter performance. Keywords—Adaptive filter, general framework, energy conservation, mean-square performance, nonstationary environment.
Least-squares function approximation
Cite
Citations (2)
Acoustic echo cancellation in multichannel is a system identification application. In real time environment, signal changes very rapidly which required adaptive algorithms such as Least Mean Square (LMS), Leaky Least Mean Square (LLMS), Normalized Least Mean square (NLMS) and average (AFA) having high convergence rate and stable [2]. LMS and NLMS are widely used adaptive algorithm due to less computational complexity and AFA used of its high convergence rate. This research is based on comparison of acoustic echo (generated in a room) cancellation through LMS, LLMS, NLMS, AFA and newly proposed average normalized leaky least mean square (ANLLMS) adaptive filters.
Echo (communications protocol)
Square (algebra)
Cite
Citations (1)
The convergence speed of the Least Mean Square (LMS) algorithm is slow for applications where the received signal is not white. An adaptive pilot filtering procedure is proposed in this paper to increase the convergence speed of the LMS algorithm. The procedure proposed uses an adaptive filter with only a few filter coefficients to filter both the received signal and the original signal for the purpose of whitening the received signal. The convergence speed of the adaptive pilot filtering procedure combined with the LMS algorithm is comparable to the Kalman algorithm for some applications. The adaptive pilot filtering procedure combined with the main LMS algorithm only requires less than 2 times that of a conventional LMS algorithm. The name of the adaptive pilot filter comes from the analogy to the pilot parachute.
Adaptive algorithm
SIGNAL (programming language)
Cite
Citations (0)
An adaptive filter based on least mean square algorithm has been realized by using universal digital signal processor TMS320C5402. The result analysis has also been given,and it provides a good reference for its implementation,so the design of hardware on LMS adaptive filter has been ensured.
Cite
Citations (0)
In this paper, adaptive filter based on adaptive algorithms like least mean square (LMS), normalised least mean square (NLMS) and recursive least square (RLS) are used for the prediction of instantaneous heart rate in ECG signal. The adaptive algorithms work on the principle of optimising the least square error by achieving wiener solution. The weights of the filter coefficients are changing, as per the changes in the signal. The performance of adaptive filter is measured by mean square error (MSE) and the prediction accuracy is observed by mean absolute error (MAE). The simulation results show that the adaptive algorithms NLMS and RLS have faster convergence rate with less number of iteration but the forecasting accuracy is higher in LMS compared to NLMS and RLS algorithms.
Wiener filter
Adaptive algorithm
Cite
Citations (3)
In this paper system identification has been done using adaptive filters. System identification is the process of identifying an unknown system form input output signal. It can be defined as the interface between real world of application and mathematical world of control theory and model abstraction. Three types of adaptive filters are used to identify the unknown system Least Mean Square (LMS), Normalized Least Mean Square (NLMS) and Recursive Least Square (RLS) algorithms. LMS has less computational complexity than NLMS and RLS while NLMS is the normalized form of LMS adaptive filter. RLS is complex algorithm but it works more efficiently. All these algorithms works on the basis of Least Mean Square Error (LMSE) and filter's weights are recursively updated as to bring output signal equal to the desired signal. These algorithms are applied to the unknown system and the simulation results are compared.
Identification
SIGNAL (programming language)
Adaptive system
Adaptive algorithm
Cite
Citations (29)
The least mean square (LMS) adaptive filter is popular owing to its simplicity but even simpler approaches are required for many real-time applications. Reduction of the complexity of the LMS filter had received attention in the area of adaptive filter. This paper proposes a method of system identification using adaptive filter which is based on a new quantised version of the LMS, namely the QX-LMS algorithm. The coefficients of the adaptive filter are adjusted automatically by an adaptive algorithm based on the input signals. This property makes the adaptive filter has an important application in system identification. The threshold parameter of the QX-LMS algorithm causes controllability and the increase of convergence property. The TMS320C55x is a 16-bit fixed-point DSP processor from Texas Instruments. It is designed for optimum performance and high code density. The realization of the proposed algorithm on DSP TMS320C55x is introduced and also its experiment results are discussed.
Finite impulse response
Cite
Citations (24)