To overcome the conflict that the adaptive regularized complex-valued NLMS algorithms cannot have op-timal performance when the regularizationparameter is large or small, a widely linear complex-valued NLMS algorithm...
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To overcome the conflict that the adaptive regularized complex-valued NLMS algorithms cannot have op-timal performance when the regularizationparameter is large or small, a widely linear complex-valued NLMS algorithm with the variable regularization parameter (VRP-WL-CNLMS) is proposed in this paper. The proposed algorithm can adaptively change the regularized parameter by exploiting a time-varying parameter that is obtained via making the power of noise-free a posteriori error minimum. A proper es-timated method is provided to compute the power of the measured noise when the noise is unknown, and the moving-average method is employed to update the regularized parameter for avoiding large fluc-tuations. Then we provide the analysis of the transient and steady-state (TAS) behaviors of the proposed algorithm. Simulation results with different input signals illustrate that the VRP-WL-CNLMS algorithm has better advantages than other algorithms, and verify the theoretical validity of TAS analysis of the pro-posed algorithm in the system identification (SI) environment. Finally, the experimental results of wind prediction show that the predicted value of the proposed algorithm has a smaller error value with the original signal than the WL-CNLMS algorithm and can predict the signal better that can support the su-periority of the proposed algorithm as well. (c) 2022 Elsevier B.V. All rights reserved.
In this paper, we propose a general robust subband adaptive filtering (GR-SAF) scheme against impulsive noise by minimizing the mean square deviation under the random-walk model with individual weight uncertainty. Spe...
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In this paper, we propose a general robust subband adaptive filtering (GR-SAF) scheme against impulsive noise by minimizing the mean square deviation under the random-walk model with individual weight uncertainty. Specifically, by choosing different scaling factors such as from the M-estimate and maximum correntropy robust criteria in the GR-SAF scheme, we can easily obtain different GR-SAF algorithms. Importantly, the proposed GR-SAF can be reduced to a variableregularization robust normalized SAF algorithm, thus having fast convergence rate and low steady-state error. Simulations in the contexts of system identification with impulsive noise and echo cancellation with double-talk have verified that the proposed GR-SAF outperforms its counterparts.
Herein, we propose a normalized subband adaptive filter (NSAF) algorithm that adjusts both the step size and regularizationparameter. Based on the random-walk model, the proposed algorithm is derived by minimizing th...
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Herein, we propose a normalized subband adaptive filter (NSAF) algorithm that adjusts both the step size and regularizationparameter. Based on the random-walk model, the proposed algorithm is derived by minimizing the mean-square deviation of the NSAF at each iteration to calculate the optimal parameters. We also propose a method for estimating the uncertainty in an unknown system. Consequently, the proposed algorithm improves performance in terms of tracking speed and misalignment. Simulation results show that the proposed NSAF outperforms existing algorithms in system identification scenarios.
This paper focuses on mixed error cost function based variable Step Size Improved Proportionate Miine Projection Algorithm (VSS-IPAPA) with Prediction Error Method (PEM). The objective is to investigate the possible i...
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ISBN:
(纸本)9789881476890
This paper focuses on mixed error cost function based variable Step Size Improved Proportionate Miine Projection Algorithm (VSS-IPAPA) with Prediction Error Method (PEM). The objective is to investigate the possible improvements in terms of performance, complexity and applicability in the real-life scenario of the updating algorithm. Mainly, the use of low complexity VSS Memory IPAPA (VSS-MIPAPA) and the addition of variable regularization parameter (VRP) are proposed in the scope of this paper. As a result, VSS-MIPAPA-VRP is proposed as an improved version of the previous VSS-IPAPA algorithm. The simulation results demonstrate that VSS-MIPAPA-VRP achieves less time complexity, higher convergence accuracy and higher precision compared to the VSS-IPAPA.
Recently, the normalized subband adaptive filter (NSAF) algorithm has attracted much attention for handling colored input signals. Based on the first-order Markov model of the optimal weight vector, this paper provide...
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Recently, the normalized subband adaptive filter (NSAF) algorithm has attracted much attention for handling colored input signals. Based on the first-order Markov model of the optimal weight vector, this paper provides some insights for the convergence of the standard NSAF. Following these insights, both the step size and the regularizationparameter in the NSAF are jointly optimized by minimizing the mean-square deviation. The resulting joint-optimization step size and regularizationparameter algorithm achieves a good tradeoff between fast convergence rate and low steady-state error. Simulation results in the context of acoustic echo cancelation demonstrate good features of the proposed algorithm.
To restore a degraded image, which has been corrupted by some kinds of noise and motion blur, a model for restoration is first presented and then an algorithm based on this model and the radial basis function network ...
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To restore a degraded image, which has been corrupted by some kinds of noise and motion blur, a model for restoration is first presented and then an algorithm based on this model and the radial basis function network (RBFN) is proposed in this paper. In the first step of this algorithm, noise is removed by using the RBFN interpolation with variable regularization parameters. In the second step, the motion blurred image is restored based on the automatic identification of the direction and length of the motion-blur. Moreover, a method, based on object extraction, for restoring local motion blurred image is given to solve the problem of the bad restoration results when the global motion-blurred characteristics are missed for local motion-blurred images. Experimental results demonstrate that the proposed method performs well when applied to general motion-blurred images, and can also be applied to the local variable speed motion-blurred images. The restoration algorithm can give accurate identification of the degraded model and good restoration results, even under the condition of a relatively large noise interference.
This paper proposes a noise reduction filter that can suppress noise components without destroying important image information. The RBF network is then used to recover a high-quality image from the degraded version, a...
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This paper proposes a noise reduction filter that can suppress noise components without destroying important image information. The RBF network is then used to recover a high-quality image from the degraded version, and the regularizationparameter is adjusted according to local image characteristics. A fast method of computing the RBF network with variable regularization parameters is developed by means of the properties of Kronecker product. (C) 2002 Published by Elsevier Science B.V.
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