Comparative Analysis and Survey of LMS and RLS Adaptive Algorithms

Ali, Farheen; Rawat, Prashant; Malvia, Sunil

doi:10.5120/ijca2017913136

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…Prior knowledge of the frequency response parameter is required to design such filters. However, in the absence of such information, it is not possible to design standard filters because of the changing nature of the filter's requirements [14]. Thus, the inability of such a system to work with limited information makes it less suitable for general application [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Signal Filter Cut-Off Frequency Determination to Enhance the Accuracy of Rail Track Irregularity Detection and Localization

et al. 2020

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A continuous condition monitoring system to detect and localize railroad track irregularities is achievable with inertial sensors onboard revenue service trains. However, the inaccurate geospatial position estimates of GPS receivers and the non-uniform sampling of inertial sensors adds noise and reduces signal strength. Consequently, the signal-to-noise ratio decreases, which leads to higher rates of false positives and false negatives. Appropriate signal filtering, alignment, and combination from multiple traversals can enhance the signal-to-noise ratio. However, it is not straightforward to determine the best cut-off frequency for the filter. This paper introduces a method that is suitable for any signal filtering approach. The frequency window of the resultant energy and variance of ensemble averaged FFTs informs the best cut-off frequency. The results affirm that a lowpass finite impulse response filter with the selected cutoff frequency progressively increases the signal-to-noise ratio with increasing filter order, thus demonstrating the effectiveness and practicality of the method.

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Section: Literature Reviewmentioning

confidence: 99%

Signal Filter Cut-Off Frequency Determination to Enhance the Accuracy of Rail Track Irregularity Detection and Localization

et al. 2020

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“…16. From the comparison between LMS algorithm and RLS algorithm, it can be seen that LMS algorithm has a simple structure and strong robustness, but its convergence performance is poor, while RLS algorithm has good convergence and strong stability. 17 Therefore, a non-stationary strong noise reduction method based on VMD and RLS adaptive filtering is proposed. The vibration signal of shearer mechanical transmission system under the background of non-stationary and strong noise is reduced, which lays a good foundation for fault diagnosis of shearer mechanical transmission system.…”

Section: Introductionmentioning

confidence: 99%

Noise reduction method for non-stationary noise of vibration signal of shearer mechanical transmission system

Mao

Zhang

Jia

et al. 2022

Noise & Vibration Worldwide

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Aiming at the problem that vibration signal of mechanical transmission system of shearer is vulnerable to be interferenced by non-stationary strong noise, a combined method with variational mode decomposition and recursive least squares (VMD-RLS) is proposed for non-stationary noise reduction. Firstly, the vibration signal is decomposed by VMD and the number of decomposition layers is determined according to the principle of minimum energy difference. Then, a dominant criterion of frequency-domain characteristic amplitude is proposed to select invalid intrinsic mode components (IMF, Intrinsic Mode Function). The proportion of the frequency-domain characteristic amplitude of each order IMF component for VMD is calculated. The invalid intrinsic mode functions are selected for reconstruction, and the noise signal after VMD decomposition is obtained. Finally, the noise signal decomposed by VMD is used as reference noise signal of RLS adaptive filtering algorithm to carry out adaptive noise reduction, and the final vibration signal after noise reduction is obtained. The wavelet de-noising method, VMD de-noising method, and VMD-RLS de-noising method are used to carry out noise reduction comparative experiment on the vibration signal of shearer. The experiment results indicate that the noise reduction method of VMD-RLS is better than VMD and wavelet de-noising method. The noise reduction method of VMD-RLS can effectively extract vibration signals from non-stationary strong noise, and provide help for the subsequent fault diagnosis and analysis of shearer mechanical transmission system.

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“…[5][6][7] In the method, estimated parameters are continuously corrected step by step until the satisfied values are obtained. 9 Another online approach is the model reference adaptive system (MRAS) technique. In other words, previous samples of output signals, error weights, and error signals must be approximated which require high memory.…”

Section: Introductionmentioning

confidence: 99%

“…In other words, previous samples of output signals, error weights, and error signals must be approximated which require high memory. 9 Another online approach is the model reference adaptive system (MRAS) technique. An actual system is considered as a reference model, while a dynamics model of system with known equations of motion but unknown parameters is chosen as an adjustable model.…”

Section: Introductionmentioning

confidence: 99%

Online and stable parameter estimation based on normalized brain emotional learning model (NBELM)

Akhormeh

Roshanian

MoradiMaryamnegari

et al. 2019

Adaptive Control & Signal

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Recently, a computational model of Amygdala based on the brain emotional learning is presented by psychologists. This brain emotional learning model (BELM) that has a neuro-inspired architecture is utilized to train the weights which are in Amygdala and Orbitofrontal. In this paper, unknown parameters of dynamic systems are estimated by developing the normalized BELM (NBELM).To this end, after proving the stability of the model output, the sufficient condition for weights convergence is extracted while the sensitivity analysis is applied for this model. In order to evaluate the performance of NBELM, in the first example, the matrices of a twin rotor MIMO system are estimated and compared with the equation error method (EEM). In the second example, the nonlinear model of a servomotor is utilized as a case study. In the third example, the performance of the NBELM in experimental systems is validated using a reaction wheel with a DC motor. An important feature of the brain emotional system is its fast response, leading the NBELM to have a high speed performance in estimating the parameters of dynamic systems. A few number of adjustable parameters and low computing complexity also cause the NBELM to be an appropriate method for online estimation of the unknown parameters of dynamic systems. KEYWORDSAmygdala computing model, BEL model, dynamic system parameter estimation, online and stable estimation Parameters of a large number of industrial systems need to be monitored online for various reasons, ranging from adaptive controls 1 to fault detection systems. 2,3 As a consequence, many research studies have been devoted to introducing novel parameters identification algorithms able to insure the stability of the system. Additionally, the proposed techniques must converge to real values as fast as possible in order to be applicable to engineering systems while high accuracy in the converging process is an evitable need in any novel method. The low complexity of the new algorithm is another challenge, leading the system identification method to be implemented with less effort and become wider. For mentioned challenges to be addressed, various investigations create new frameworks in terms of problem formulation, methodology development, theoretical foundation, software tools, and hardware modules. 4Int J Adapt Control Signal Process. 2019;33:1047-1065.wileyonlinelibrary.com/journal/acs

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Comparative Analysis and Survey of LMS and RLS Adaptive Algorithms

Cited by 4 publications

References 7 publications

Signal Filter Cut-Off Frequency Determination to Enhance the Accuracy of Rail Track Irregularity Detection and Localization

Signal Filter Cut-Off Frequency Determination to Enhance the Accuracy of Rail Track Irregularity Detection and Localization

Noise reduction method for non-stationary noise of vibration signal of shearer mechanical transmission system

Online and stable parameter estimation based on normalized brain emotional learning model (NBELM)

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