In this paper, a least Incosh (Llncosh) algorithm is derived by utilizing the Incosh cost function. The Incosh cost is characterized by the natural logarithm of hyperbolic cosine function, which behaves like a hybrid ...
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In this paper, a least Incosh (Llncosh) algorithm is derived by utilizing the Incosh cost function. The Incosh cost is characterized by the natural logarithm of hyperbolic cosine function, which behaves like a hybrid of the mean square error (MSE) and mean absolute error (MAE) criteria depending on adjusting a positive parameter A. Hence, the Llncosh algorithm performs like the least mean square (lms) algorithm for small errors and behaves as the sign-error lms (Slms) algorithm for large errors. It provides comparable performance to the lms algorithm in Gaussian noise. When compared with several existing robust approaches, the superior steady-state performance and stronger robustness can be attained in impulsive noise. The mean behavior, mean-square behavior and steady-state performance analyses of the proposed algorithm are also provided. In addition, aiming to acquire a compromise between fast initial convergence rate and satisfactory steady-state performance, we introduce a variable-A Llncosh (VLlncosh) scheme. Lastly, in order to resist the sparsity of the acoustic echo path, an improved proportionate least Incosh (PLlncosh) algorithm is presented. The good performance against impulsive noise and theoretical results of the proposed algorithm are validated by simulations. (C) 2019 Elsevier B.V. All rights reserved.
In recent years, there is a growing effort in the learning algorithms area to propose new strategies to detect and exploit sparsity in the model parameters. In many situations, the sparsity is hidden in the relations ...
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ISBN:
(纸本)9781538646595
In recent years, there is a growing effort in the learning algorithms area to propose new strategies to detect and exploit sparsity in the model parameters. In many situations, the sparsity is hidden in the relations among these coefficients so that some suitable tools are required to reveal the potential sparsity. This work proposes a set of lms-type algorithms, collectively called Feature lms (F-lms) algorithms, setting forth a hidden feature of the unknown parameters, which ultimately would improve convergence speed and steady-state mean-squared error. The key idea is to apply linear transformations, by means of the so-called feature matrices, to reveal the sparsity hidden in the coefficient vector, followed by a sparsity-promoting penalty function to exploit such sparsity. Some F-lms algorithms for lowpass and highpass systems are also introduced by using simple feature matrices that require only trivial operations. Simulation results demonstrate that the proposed F-lms algorithms bring about several performance improvements whenever the hidden sparsity of the parameters is exposed.
In this paper, Speech improvement has been implemented with versatile Adaptive filtering approach. The removal of undesirable signal means noise from speech signals has various applications. This paper depicts effecti...
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ISBN:
(纸本)9781728120942
In this paper, Speech improvement has been implemented with versatile Adaptive filtering approach. The removal of undesirable signal means noise from speech signals has various applications. This paper depicts effective calculations for contents of noise and speech after removal of noise. lms (least mean square) based versatile adaptive filtering and UNANR (Unbiased and Normalized adaption noise reduction) based versatile adaptive filtering has been actualized for the functional unwanted speech signal. To estimate the limit of the proposed execution, Peak sign to noise ratio proportion change (PSNR), root mean square error is figured out. Performance analysis has been carried out with respect to formant analysis also. The outcome demonstrates the execution of the UNANR based calculation is better to that of the lms calculation in speech enhancement applications.
This paper presents the reduction of baseline wander noise found in ECG signals. The reduction has been done using wavelet transform inspired error normalized step size least mean square (ENSS-lms) algorithm. We are p...
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ISBN:
(纸本)9789811088483;9789811088476
This paper presents the reduction of baseline wander noise found in ECG signals. The reduction has been done using wavelet transform inspired error normalized step size least mean square (ENSS-lms) algorithm. We are presenting a wavelet decomposition-based filtering technique to minimize the computational complexity along with the good quality of output signal. The MATLAB simulation results validate the good noise rejection in output signal by analyzing parameters, excess mean square error (EMSE) and misadjustment.
By building a nonlinear function relationship between mu and the error signal e(n), this paper presents a new variable step size lms(Least-Mean-Square)adaptive filtering algorithm, and analyzes the algorithm with vari...
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ISBN:
(纸本)9781538606124
By building a nonlinear function relationship between mu and the error signal e(n), this paper presents a new variable step size lms(Least-Mean-Square)adaptive filtering algorithm, and analyzes the algorithm with various parameters alpha and beta. This step size algorithm avoids the shortage of adjusting step size of SVSlms (variable step size lms based on Sigmoid function). Also in the process of the adaptive steady state it has the virtue of e(n) slightly changing close to zero. Theoretical analysis and computer simulations show that with the proposed algorithm, convergence rate can be improved than the former.
This paper presents a new variable step size lms (Least-Mean-Square) adaptive filtering algorithm in adaptive echo cancellation. This step size algorithm builds a nonlinear function relationship between the step-size ...
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ISBN:
(纸本)9781509039449
This paper presents a new variable step size lms (Least-Mean-Square) adaptive filtering algorithm in adaptive echo cancellation. This step size algorithm builds a nonlinear function relationship between the step-size parameter and the error signal. Theoretical analysis and computer simulations show that convergence rate can be improved than the former by the proposed algorithm. The new algorithm has good performance. It is applied in the filtering process of adaptive echo cancellation. The filtering effect is good.
In this paper we are focused on the Multi-Carrier Code Division Multiple Access (MC-CDMA) equalization problem. The equalization is performed using the Minimum Mean Square Error (MMSE) and Zero Forcing (ZF) equalizer ...
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In this paper we are focused on the Multi-Carrier Code Division Multiple Access (MC-CDMA) equalization problem. The equalization is performed using the Minimum Mean Square Error (MMSE) and Zero Forcing (ZF) equalizer based on the identified parameters representing the indoor scenario (European Telecommunications Standards Institute Broadband Radio Access Networks (ETSI BRAN A) channel model), and outdoor scenario (ETSI BRAN E channel model). These channels are normalized for fourth-generation mobile communication systems. However, for such high-speed data transmissions, the channel is severely frequency-selective due to the presence of many interfering paths with different time delays. The identification problem is performed using the Least Mean Squares (lms) algorithm and the Takagi-Sugueno (TS) fuzzy system. The comparison between these techniques, for the channel identification, will be made for different Signal to Noise Ratios (SNR).
Underwater acoustic channels are recognized for being one of the most difficult propagation media due to considerable difficulties such as： multipath, ambient noise, time-frequency selective fading. The exploitation ...
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Underwater acoustic channels are recognized for being one of the most difficult propagation media due to considerable difficulties such as： multipath, ambient noise, time-frequency selective fading. The exploitation of sparsity contained in underwater acoustic channels provides a potential solution to improve the performance of underwater acoustic channel estimation. Compared with the classic 10 and 11 norm constraint lms algorithms, the p-norm-like （Ip） constraint lms algorithm proposed in our previous investigation exhibits better sparsity exploitation performance at the presence of channel variations, as it enables the adaptability to the sparseness by tuning of p parameter. However, the decimal exponential calculation associated with the p-norm-like constraint lms algorithm poses considerable limitations in practical application. In this paper, a simplified variant of the p-norm-like constraint lms was proposed with the employment of Newton iteration m to approximate the decimal exponential calculation. Num simulations and the experimental results obtained in physical shallow water channels demonstrate the effectiveness of the proposed method compared to traditional norm constraint lms algorithms.
The design of adaptive nonlinear filters has sparked a great interest in the machine learning community. The present paper aims to present some recent developments in nonlinear adaptive filtering. It provides an in-de...
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The design of adaptive nonlinear filters has sparked a great interest in the machine learning community. The present paper aims to present some recent developments in nonlinear adaptive filtering. It provides an in-depth analysis of the performance and complexity of a class of kernel filters based on the least-mean-squares algorithm. A key feature that underlies kernel algorithms is that they map the data in a high-dimensional feature space where linear filtering is performed. The arithmetic operations are carried out in the initial space via evaluation of inner products between pairs of input patterns called kernels. The SNR improvement and the convergence speed of kernel-based least-mean-squares filters are evaluated on two types of applications: time series prediction and cardiac artifacts extraction from magnetoencephalographic data.
In a maglev flywheel system, an unbalanced interference signal synchronous with the flywheel frequency will be produced because the flywheel rotor is uneven. This signal can reduce system control precision and stabili...
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In a maglev flywheel system, an unbalanced interference signal synchronous with the flywheel frequency will be produced because the flywheel rotor is uneven. This signal can reduce system control precision and stability, and make the system produce unbalance force vibration. Therefore, the signal should be filtered to reduce the effects on the control system. A vibration switching compensation control strategy is proposed based on the standard least mean squares (lms) algorithm and one proportional-integral-derivative (PID) controller. The flywheel's rotational frequency is measured in real time as the switching reference, and different lms algorithm step sizes and different PID controller parameters are adopted in different rotational frequencies. Simulation and experimental results verify the feasibility and effectiveness of the switch compensation strategy, which can reduce the control current amplitude and make the flywheel rotate around the inertial principal axis as much as possible. (C) 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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