共查询到20条相似文献,搜索用时 31 毫秒
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Shengkui Zhao Douglas L. Jones Suiyang Khoo Zhihong Man 《Circuits, Systems, and Signal Processing》2014,33(7):2251-2265
The least-mean-square-type (LMS-type) algorithms are known as simple and effective adaptation algorithms. However, the LMS-type algorithms have a trade-off between the convergence rate and steady-state performance. In this paper, we investigate a new variable step-size approach to achieve fast convergence rate and low steady-state misadjustment. By approximating the optimal step-size that minimizes the mean-square deviation, we derive variable step-sizes for both the time-domain normalized LMS (NLMS) algorithm and the transform-domain LMS (TDLMS) algorithm. The proposed variable step-sizes are simple quotient forms of the filtered versions of the quadratic error and very effective for the NLMS and TDLMS algorithms. The computer simulations are demonstrated in the framework of adaptive system modeling. Superior performance is obtained compared to the existing popular variable step-size approaches of the NLMS and TDLMS algorithms. 相似文献
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A new robust computationally efficient variable step-size LMS algorithm is proposed and it is applied for secondary path (SP)
identification of feedforward and feedback active noise control (ANC) systems. The proposed variable step-size Griffiths’
LMS (VGLMS) algorithm not only uses a step-size, but also the gradient itself, based on the cross-correlation between input
and the desired signal. This makes the algorithm robust to both stationary and non-stationary observation noise and the additional
computational load involved for this is marginal. Further, in terms of convergence speed and error, it is better than those
by the Normalized LMS (NLMS) and the Zhang’s method (Zhang in EURASIP J. Adv. Signal Process. 2008(529480):1–9, 2008). The
convergence rate of the feedforward and feedback ANC systems with the VGLMS algorithm for SP identification is faster (by
a factor of 2 and 3, respectively) compared with that using NLMS algorithm. For feedforward ANC, its convergence rate is faster
(3 times) compared with Akhtar’s algorithm (Akhtar in IEEE Trans Audio Speech Lang Process 14(2), 2006). Also, for higher
main path lengths compared with SP, the proposed algorithm is computationally efficient compared with Akhtar’s algorithm. 相似文献
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迭代变步长LMS算法及性能分析 总被引:1,自引:0,他引:1
针对固定步长LMS(Least Mean Square)算法(FXSSLMS)不能同时满足快速收敛和小稳态失调误差的问题,该文提出了迭代变步长LMS算法(IVSSLMS)。与已有的变步长LMS算法(VSSLMS)不同,该算法的步长因子不再是由输出误差信号控制,而是建立了与迭代时间的改进Logistic函数非线性关系,克服了定步长算法收敛慢及已有变步长算法抗噪声干扰能力差的问题。最后从理论上分析了算法的性能,给出了其参数取值方法。理论分析和仿真均表明,所提算法能够在快速收敛情况下获得小的稳态失调误差,在有色噪声干扰下稳态失调误差比已有算法降低了约7 dB。 相似文献
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Partial update LMS algorithms 总被引:3,自引:0,他引:3
Partial updating of LMS filter coefficients is an effective method for reducing computational load and power consumption in adaptive filter implementations. This paper presents an analysis of convergence of the class of Sequential Partial Update LMS algorithms (S-LMS) under various assumptions and shows that divergence can be prevented by scheduling coefficient updates at random, which we call the Stochastic Partial Update LMS algorithm (SPU-LMS). Specifically, under the standard independence assumptions, for wide sense stationary signals, the S-LMS algorithm converges in the mean if the step-size parameter /spl mu/ is in the convergent range of ordinary LMS. Relaxing the independence assumption, it is shown that S-LMS and LMS algorithms have the same sufficient conditions for exponential stability. However, there exist nonstationary signals for which the existing algorithms, S-LMS included, are unstable and do not converge for any value of /spl mu/. On the other hand, under broad conditions, the SPU-LMS algorithm remains stable for nonstationary signals. Expressions for convergence rate and steady-state mean-square error of SPU-LMS are derived. The theoretical results of this paper are validated and compared by simulation through numerical examples. 相似文献
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变步长LMS自适应滤波算法通过构造合适的步长因子有效的解决了传统LMS算法收敛速度和稳态误差相矛盾的问题.变换域LMS自适应滤波算法通过正交变换降低了输入信号矩阵的相关性,提高了算法的收敛速度.将这两种算法相结合,提出了一种新的基于小波变换的变步长LMS自适应滤波算法.仿真结果表明,该算法无论是收敛速度还是稳态误差都有了很大的提高. 相似文献
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Noise-constrained least mean squares algorithm 总被引:1,自引:0,他引:1
Yongbin Wei Gelfand S.B. Krogmeier J.V. 《Signal Processing, IEEE Transactions on》2001,49(9):1961-1970
We consider the design of an adaptive algorithm for finite impulse response channel estimation, which incorporates partial knowledge of the channel, specifically, the additive noise variance. Although the noise variance is not required for the offline Wiener solution, there are potential benefits (and limitations) for the learning behavior of an adaptive solution. In our approach, a Robbins-Monro algorithm is used to minimize the conventional mean square error criterion subject to a noise variance constraint and a penalty term necessary to guarantee uniqueness of the combined weight/multiplier solution. The resulting noise-constrained LMS (NCLMS) algorithm is a type of variable step-size LMS algorithm where the step-size rule arises naturally from the constraints. A convergence and performance analysis is carried out, and extensive simulations are conducted that compare NCLMS with several adaptive algorithms. This work also provides an appropriate framework for the derivation and analysis of other adaptive algorithms that incorporate partial knowledge of the channel 相似文献
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针对固定步长的归一化LMS算法(NLMS)存在不能同时兼顾收敛速度与稳态误差的问题,本文提出一种依据迭代系数状态因子进行分段的变步长NLMS算法。该变步长NLMS算法采用迭代系数状态因子作为表征迭代系数与实际系数的逼近状态的指标。当迭代系数状态因子值大于1,则说明迭代系数有偏离真实系数的趋势,此时采用步长因子较大的变步长方案;反之,说明迭代系数有逼近真实系数的趋势,应该采样步长因子较小的变步长方案。这样的自适应选择措施使得算法具有较强的收敛能力。理论分析和实验表明:在同样实验条件下,本文算法能够获得比其他文献更快的收敛速度和更小的稳态误差。 相似文献
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A novel kurtosis driven variable step-size adaptive algorithm 总被引:6,自引:0,他引:6
A new variable step-size LMS filter is introduced. The time-varying step-size sequence is adjusted, utilizing the kurtosis of the estimation error, therefore reducing performance degradation due to the existence of strong noise. The convergence properties of the algorithm are analyzed, and an adaptive kurtosis estimator that takes into account noise statistics and optimally adapts itself is also presented. Simulation results confirm the algorithm's improved performance and flexibility 相似文献
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收发隔离是干扰机系统中的关键问题,若隔离度不够,轻则削弱干扰机效率,重则造成自激使干扰机不能正常工作。传统的最小均方(LMS)算法应用到基于自适应系统辨识的收发隔离中时,由于精度不够,隔离性能并不理想。针对该问题,提出一种分段变步长归一化LMS(NLMS)算法。该算法利用迭代系数状态因子对迭代系数与真实系数逼近状态进行分段,当迭代系数状态因子处于不同的区间时,采用不同的变步长方案。理论分析和仿真证明:与基于其他LMS 算法的隔离方案相比,基于分段变步长NLMS 算法的收发隔离方案隔离性能有较大的改善。 相似文献
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The normalized least mean square (NLMS) algorithm is an important variant of the classical LMS algorithm for adaptive linear filtering. It possesses many advantages over the LMS algorithm, including having a faster convergence and providing for an automatic time-varying choice of the LMS stepsize parameter that affects the stability, steady-state mean square error (MSE), and convergence speed of the algorithm. An auxiliary fixed step-size that is often introduced in the NLMS algorithm has the advantage that its stability region (step-size range for algorithm stability) is independent of the signal statistics. In this paper, we generalize the NLMS algorithm by deriving a class of nonlinear normalized LMS-type (NLMS-type) algorithms that are applicable to a wide variety of nonlinear filter structures. We obtain a general nonlinear NLMS-type algorithm by choosing an optimal time-varying step-size that minimizes the next-step MSE at each iteration of the general nonlinear LMS-type algorithm. As in the linear case, we introduce a dimensionless auxiliary step-size whose stability range is independent of the signal statistics. The stability region could therefore be determined empirically for any given nonlinear filter type. We present computer simulations of these algorithms for two specific nonlinear filter structures: Volterra filters and the previously proposed class of Myriad filters. These simulations indicate that the NLMS-type algorithms, in general, converge faster than their LMS-type counterparts 相似文献
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文中介绍了自适应滤波算法的原理和干扰抵消器工作原理,并将LMS算法、NLMS算法和变步长LMS算法分别应用在了干扰抵消器中进行了仿真。仿真的结果表明,三种自适应算法运用到了干扰抵消器中,都可以很好地滤除干扰,提取有用信号。其中运用了变步长LMS算法的干扰抵消器无论在收敛速度和滤波性能上,都要强于其他两种算法。 相似文献
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变换域是一种在强相关信号输入时加快自适应算法收敛的方法,但仍然存在收敛速度的要求与稳态失调的要求相矛盾的问题。本文在变换域最小均方误差算法(transform domain LMS, TDLMS)的基础上提出了一种改进的变步长方案,其变步长因子受到误差自相关的控制,消除了不相关的观测噪声的影响。本文分别在平稳和非平稳状态下,对算法的收敛和稳态性能进行理论分析,并给出了最佳的算法参数。仿真设置相同的稳态误差,结果表明本文算法在平稳状态下比固定步长的算法提前1300点收敛,在非平稳状态下提前1400点收敛,且与文献中其它变步长的算法相比收敛速度均有提升。 相似文献