A Stabilized Multichannel Fast RLS Algorithm for Adaptive Transmultiplexer Receivers

Chang, Dong Shang; Chiu, Hsien-Cheng

doi:10.1007/s00034-009-9131-6

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…Aiming to minimize the sum of the squares of the difference between the desired signal and the filter output, least square (LS) algorithm could use recursive form to solve least-squares at the moment the latest sampling value is acquired [10]. The filter output and the error function of RLS algorithm is…”

Section: B Rls Adaptive Filter Algorithmmentioning

confidence: 99%

Comparison of LMS and RLS Algorithm for Active Vibration Control of Smart Structures

Huang

Gao

Shao

et al. 2011

2011 Third International Conference on Measuring Technology and Mechatronics Automation

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Control algorithm is one of the key elements for active vibration suppression of the space flexible structure based on smart materials. As least mean square (LMS) algorithm and recursive least square (RLS) algorithm are two fundamental algorithms for adaptive feed-forward filter algorithm drawing wide attention, this paper focuses on the algorithm process analysis and performance comparison of the two adaptive algorithms based on finite impulse response (FIR) structure. The design and related characteristic analysis of the controller is given as well as the realization process based on the theoretical analysis. Simulation is done using Matlab 7.0 to verify the correctness of the theoretical analysis. The simulation comparison between the two type algorithms shows that RLS algorithm has faster convergence and better control performance than LMS algorithm. On the basis of the simulation analysis, the actual vibration suppression experiment is done on the constructed experimental platform for piezoelectric flexible beam, and experiment result confirms the simulation effects.

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Section: B Rls Adaptive Filter Algorithmmentioning

confidence: 99%

Comparison of LMS and RLS Algorithm for Active Vibration Control of Smart Structures

Huang

Gao

Shao

et al. 2011

2011 Third International Conference on Measuring Technology and Mechatronics Automation

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“…Finding non-trivial solutions is difficult since conditions (2) lead to non-linear equations with respect to filter coefficients. Interesting approximations were suggested in [13][14][15][16][17][18][19][20][21][22][23][24][25][26] to solve this or similar problems. Frequently, numerical procedures are used to determine filter coefficients by minimizing residuals of equations obtained from the perfect reconstruction conditions.…”

Section: Transmultiplexersmentioning

confidence: 99%

Archives of Control Sciences

Ziółko¹,

Nowak²

2018

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The designing of transmultiplexer systems relies on determining filters for the transmitter and receiver sides of multicarrier communication system. The perfect reconstruction conditions lead to the bilinear equations for FIR filter coefficients. Generally there is no way of finding all possible solutions. This paper describes methods of finding a large family of solutions. Particular attention is devoted to obtaining algorithms useful in fixed-point arithmetic needed to design the integer filters. As a result, the systems perform perfect reconstruction of signals. Additionally, a simple method is presented to transform any transmultiplexer into an unlimited number of different transmultiplexers. Finally, two examples of integer filters that meet perfect reconstruction conditions are shown. The first illustrates a FIR filter which does not require multiplications. The frequency properties of filters and signals are discussed for the second example.

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“…According to the different ways of extracting desired signal, adaptive filter can be divided into four types: identification, inverse model, prediction, and interference elimination. There are various adaptive algorithms for adaptive filter design, including LMS algorithm, Recursive Least Square (RLS) algorithm [1], Neural Network algorithm [2] and so forth. Among them, LMS algorithm introduced by Widrow and Hoff is extensively employed for its small calculation, strong robustness and ease of implementation.…”

Section: Introductionmentioning

confidence: 99%

A Variable Step Size LMS Based on Sparsity for System Identification

Chen¹,

Ju²,

Fan³

et al. 2013

AMM

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In many practical applications, the impulse responses of the unknown system are sparse. However, the standard Least Mean Square (LMS) algorithm does not make full use of the sparsity, and the general sparse LMS algorithms increase steady-state error because of giving much large attraction to the small factor. In order to improve the performance of sparse system identification, we propose a new algorithm which introduces a variable step size method into the Reweighted Zero-Attracting LMS (RZALMS) algorithm. The improved algorithm, whose step size adjustment is controlled by the instantaneous error, is called Variable step size RZALMS (V-RZALMS). The variable step size leads to yielding smaller steady-state error on the premise of higher convergent speed. Moreover, the sparser the system is, the better the V-RZALMS performs. Three different experiments are implemented to validate the effectiveness of our new algorithm.

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A Stabilized Multichannel Fast RLS Algorithm for Adaptive Transmultiplexer Receivers

Cited by 4 publications

References 24 publications

Comparison of LMS and RLS Algorithm for Active Vibration Control of Smart Structures

Comparison of LMS and RLS Algorithm for Active Vibration Control of Smart Structures

Archives of Control Sciences

A Variable Step Size LMS Based on Sparsity for System Identification

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