Solving a high-dimension system identification problem could involve significant challenges in terms of complexity and accuracy of the solution. Due to the large parameter space, a decomposition-based approach fits ve...
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ISBN:
(纸本)9781728189215
Solving a high-dimension system identification problem could involve significant challenges in terms of complexity and accuracy of the solution. Due to the large parameter space, a decomposition-based approach fits very well in this context. This was the idea behind the recently developed iterative Wiener filter for multilinear forms, which reformulates the problem using a combination of shorter filters. Nevertheless, there are inherent limitations related to the Wiener solution, while the least-mean-square (lms) adaptive filter would represent a more practical alternative. Consequently, in this paper, we develop lms-based algorithms for multilinear forms, in the context of a multiple-input/single-output system identification problem. Simulation results indicate the good performance of the proposed algorithms, especially in terms of their fast convergence features.
A desired signal corrupted by additive noise can often be recovered by an adaptive noise canceller using the least mean squares (lms) algorithm. A major disadvantage of the lms algorithm is its excess mean-squared err...
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A desired signal corrupted by additive noise can often be recovered by an adaptive noise canceller using the least mean squares (lms) algorithm. A major disadvantage of the lms algorithm is its excess mean-squared error, or misadjustment, which increases linearly with the desired signal power. This leads to degrading performance when the desired signal exhibits large power fluctuations and is a serious problem in many speech processing applications. This work considers two modified lms algorithms, the weighted sum and sum methods, designed to solve this problem by reducing the size of the steps in the weight update equation when the desired signal is strong. The weighted sum method is derived;from an optimal method (also developed in this work), which is not generally applicable because it requires quantities unavailable in a practical system. The previously proposed, but ad hoc, sum method is analyzed and compared to the weighted sum method, Analysis of the two modified lms algorithms indicates that either one provides substantial improvements in the presence of strong desired signals and similar performance in the presence of weak desired signals, relative to the unmodified lms algorithm. Computer simulations with both uncorrelated Gaussian noise and speech signals confirm the results of the analysis and demonstrate the effectiveness of the modified algorithms. The modified lms algorithms are particularly suited for signals (such as speech) that exhibit large fluctuations in short-time power levels.
Because of the inherent trade-off between source distortion and channel distortion in video transmission systems, joint optimization between bit-rate and distortion is still a challenging task. In this paper, we propo...
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Because of the inherent trade-off between source distortion and channel distortion in video transmission systems, joint optimization between bit-rate and distortion is still a challenging task. In this paper, we propose a method where the bit-rate allocation between source and channel encoder is controlled by the estimated end-to-end distortion at the encoder. The distortion estimation scheme is based on the adaptive forward linear predictor using least-mean square (lms) algorithm. The forward predictor used the past values of actual end-to-end distortion to estimate the current distortion. The results show good estimate of end-to-end distortion and the proposed scheme improves video quality as compared to a standard rate control of H.264/AVC. The proposed scheme dynamically allocates the source encoder bits based on the estimated distortion.
This paper studies the stochastic behavior of the lms and Nlms algorithms for a system identification framework when the input signal is a cyclostationary white Gaussian process. The input cyclostationary signal is mo...
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This paper studies the stochastic behavior of the lms and Nlms algorithms for a system identification framework when the input signal is a cyclostationary white Gaussian process. The input cyclostationary signal is modeled by a white Gaussian random process with periodically time-varying power. Mathematical models are derived for the mean and mean-square-deviation (MSD) behavior of the adaptive weights with the input cyclostationarity. These models are also applied to the non-stationary system with a random walk variation of the optimal weights. Monte Carlo simulations of the two algorithms provide strong support for the theory. Finally, the performance of the two algorithms is compared for a variety of scenarios.
This paper compares the performance analysis of our proposed New Time Varying lms (NTVlms) algorithm with other well-known adaptive algorithms such as lms, Nlms, RVSSlms and TVlms algorithm. These algorithms have been...
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ISBN:
(纸本)9781479981144
This paper compares the performance analysis of our proposed New Time Varying lms (NTVlms) algorithm with other well-known adaptive algorithms such as lms, Nlms, RVSSlms and TVlms algorithm. These algorithms have been tested for their adaptive noise cancellation capabilities in the context of Speech signals corrupted by four variants of noise signals viz White Gaussian noise, Conference noise, Engine noise and Traffic noise. Performance of these algorithms is analyzed based on output SNR. The computer simulation results show that the performance of proposed algorithm is better compared to other algorithms in White Gaussian noise Environment. For the other three noise environments, Nlms algorithm performs well.
This paper presents the implementation of adaptive algorithms like Least Mean Square (lms) and Normalized Least Mean Square (Nlms) in the frequency domain and their comparison to that implemented in the time domain. A...
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ISBN:
(纸本)9781479942367
This paper presents the implementation of adaptive algorithms like Least Mean Square (lms) and Normalized Least Mean Square (Nlms) in the frequency domain and their comparison to that implemented in the time domain. Adaptive filtering using adaptive algorithm in frequency domain can be done by taking Fourier Transform of input signal and independent weigh coefficient. By frequency domain approach significant reduction in mathematical computation has been achieved. An expression for updating the weights is implemented in the frequency domain and statistical analysis has been performed. The SNR (Signal to Noise Ratio) is a parameter used to evaluate the performance with different step size. The signal power and noise power has also been calculated using MATLAB. The SNR of output signal rises about 8-9 times in frequency domain than the time domain.
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.
A New Time Varying lms (NTVlms) algorithm is proposed and its performance is analysed. The proposed algorithm is applied to adaptive noise cancellation system. The investigations have indicated better performance of t...
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ISBN:
(纸本)9781467367257
A New Time Varying lms (NTVlms) algorithm is proposed and its performance is analysed. The proposed algorithm is applied to adaptive noise cancellation system. The investigations have indicated better performance of the proposed NTVlms algorithm as compared to lms, Nlms, RVSSlms, NVSSlms and TVlms algorithm for stationary signal in terms convergence rate and output SNR except MVSSlms algorithm. For Speech signals proposed NTVlms algorithm is better compared to other algorithms in White Gaussian noise Environment and Nlms algorithm performs well in Conference noise, Engine noise & Traffic noise environments in terms of output SNR.
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.
A new variable-step-size lms algorithm is proposed, and it performance is analyzed. Simulation results indicate that the performance is superior to that of existing VSS algorithm and Nlms algorithm. The proposed algor...
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ISBN:
(纸本)9781424421787
A new variable-step-size lms algorithm is proposed, and it performance is analyzed. Simulation results indicate that the performance is superior to that of existing VSS algorithm and Nlms algorithm. The proposed algorithm is then applied to adaptive noise jamming cancellation system;the computer simulation shows superior performance over the Nlms algorithm and MVSS algorithm.
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