This paper presents the Variable Step Size Least Mean Square algorithm formulated in frequency domain by taking the (Fast Fourier Transform) FFT of signal obtained from filter. This way the algorithms performed better...
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ISBN:
(纸本)9781479918195
This paper presents the Variable Step Size Least Mean Square algorithm formulated in frequency domain by taking the (Fast Fourier Transform) FFT of signal obtained from filter. This way the algorithms performed better than its implementation in time domain in terms of Signal to Noise Ratio (SNR). The algorithms implemented in MATLAB with different colored noise surroundings. To evaluate the performance of the algorithm its comparison has been done with time domain. The algorithm has given 5-44% increased SNR compared to that implemented in time domain with different type of colored noises. The algorithm has also been tested in frequency domain for different step sizes.
Aiming at the noise interference problem in wing fatigue tests, this paper improves the traditional lmsalgorithm using the variational Versoria function and the variational Gaussian function. Additionally, this paper...
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Aiming at the noise interference problem in wing fatigue tests, this paper improves the traditional lmsalgorithm using the variational Versoria function and the variational Gaussian function. Additionally, this paper proposes a variable step-size lms (vss-lms) filtering algorithm based on the composite function (Cvss-lms). The composite function combines the variational Versoria function and the variational Gaussian function to describe the nonlinear relationship between the iteration step size and the error. To adapt to environments with different signal-to-noise ratios, the algorithm replaces the fixed parameters with a combination of current and previous errors, thus enabling adaptive adjustment of the parameters. Moreover, a step-size dynamic constraint rule is proposed to further improve the stability of the algorithm. The algorithm is normalized using a combination of the cumulative sum of error squares, the mean square error (MSE), and the power of the input signal, which reduces the sensitivity to the input signal amplitude. The above parts finally constitute the adaptive Cvss-lms (ACvss-lms) filtering algorithm. The convergence of the ACvss-lms algorithm is verified through theoretical derivation. The ACvss-lms algorithm is experimentally analyzed by using the simulation data generated by MATLAB and the actual data collected from the wing fatigue test, and the results show that the ACvss-lms algorithm proposed in this paper has a faster convergence speed and lower steady-state error compared to other algorithms.
Estimation of power system harmonics and their elimination is an interdisciplinary area of interest for many researchers. This paper presents Variable Step Size Least Mean Square (vss-lms) approach for harmonics estim...
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Estimation of power system harmonics and their elimination is an interdisciplinary area of interest for many researchers. This paper presents Variable Step Size Least Mean Square (vss-lms) approach for harmonics estimation and Shunt Active Power Filter (SAPF) with two-level Hysteresis Current Control (HCC) technique for their elimination in a three-phase distribution system. In the estimation process, the weight is updated using vss-lms algorithm. Harmonics components are estimated from the updated weights. In order to mitigate harmonics produced by the nonlinear load connected in a three-phase distribution system, SAPF with two-level HCC is proposed. A three-phase insulated gate bipolar transistor (IGBT) based current controlled voltage source inverter (CC VSI) with a dc bus capacitor is used as an active power filter. The first step is to calculate SAPF reference currents from the sensed nonlinear load currents by applying the synchronous detection method and then the reference currents are fed to the proposed controller for generation of switching signals. The nonlinear load consists of one three-phase and one single-phase diode rectifier feeding R-L load, so that the effectiveness of the two-level HCC scheme to compensate for unbalanced nonlinear load can be tested. Various simulation results are presented to verify the good behavior of the SAPF with proposed two levels HCC. (C) 2013 Published by Elsevier Ltd.
A new variable step-size least mean squares (vss-lms) algorithm for the estimation of frequency-selective communications channels is herein presented. In contrast to previous works, in which the step-size adaptation i...
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A new variable step-size least mean squares (vss-lms) algorithm for the estimation of frequency-selective communications channels is herein presented. In contrast to previous works, in which the step-size adaptation is based on the instantaneous samples of the error signal, this algorithm is derived on the basis of analytical minimisation of the ensemble-averaged mean-square weight error. A very simple rule for step-size adaptation is obtained, using a small number of communication system parameters. This is another significant difference from other proposals, in which a large number of control parameters should be tuned for proper use. The algorithm here proposed is shown to be applicable to both time-varying and time-invariant scenarios. While the lack of a termination rule for step-size adaptation is a common characteristic of other schemes, the algorithm here presented adopts a criterion for stopping the step-size adaptation that assures optimal steady-state performance and leads to large computational savings. A simulation-based performance comparison with other vss-lms schemes is provided, including their application to maximum likelihood sequence estimation receivers using per survivor processing (MLSE/PSP). The results show that the algorithm proposed in this work has good performance characteristics and a very low computational cost, specially in the application to MLSE/PSP receivers. Besides, this algorithm is shown to be robust to changes in the signal-to-noise ratio (SNR).
Faced with the problem of existing active control systems failing to obtain a reference signal and reducing or deteriorating the effectiveness of interior noise control, an Auxiliary Active Noise Control system (AANC)...
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Faced with the problem of existing active control systems failing to obtain a reference signal and reducing or deteriorating the effectiveness of interior noise control, an Auxiliary Active Noise Control system (AANC) based on signal reconstruction technology is proposed, which main contains offline update of multi-network weights and online AANC controller. Firstly, considering the non-linear and non-stationary of the interior noise, and aiming to balance the modeling efficiency and accuracy of algorithms, based on the data fusion and compression sensing technology, a signal reconstruction model for multi-networks based on decomposition optimization is design;Then, in the online AANC controller, the offline calculation of the weight of signal reconstruction model is used to obtain the reconstruction reference signal of the control position, and based on the reconstructed signal, the controller achieves adaptive suppression of passenger ear-sides noise through the vss-lms algorithm. Finally, the effectiveness of the proposed AANC is verified using real vehicle data. The results show that the proposed system achieves the balance the modeling efficiency and accuracy, and is superior to existing ANC system in terms of robustness, guaranteeing the stable operation of the interior noise control.
When the primary reference signal obtained by the existing Active noise control (ANC) system is not accurate, the control effect of interior noise will be reduced or even fail. Considering the faulttolerate and robust...
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When the primary reference signal obtained by the existing Active noise control (ANC) system is not accurate, the control effect of interior noise will be reduced or even fail. Considering the faulttolerate and robustness of the system, the study proposes an adaptive nonlinear ANC system for interior noise, which contains noise signal decomposition, multi-network reconstruction model and Variable step-size lms (vss-lms) algorithm. The noise signal decomposition method is used to address the non-stationary of the interior noise;Based on the signal components, the multinetwork model for the noise signal reconstruction of passenger ear-sides is designed, which is pre-trained by a restricted Boltzmann machine for improved reconstruction accuracy and realize the adaptive extraction of signal features;And then based on the reconstruction signal components, the controller weights of corresponding components are adaptively updated by the vsslmsalgorithm to control the passenger ear-sides noise. The effectiveness of the proposed adaptive nonlinear ANC system is validated using noise signal sources collected from a vehicle. Compared with the different ANC systems, the proposed system is superior in terms of faulttolerant and robustness, which can guarantee stable work of the interior noise control.
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