【正文】 CA 增 加了對 N 的 補充和 N IF 的討論 .對于 VS LMS 算法 ，其增加 了 ： 3N 乘法， N 的添加 ，以及決定至少 2N IF 。 （ b） 比較與 VS LMS 算法 [6]：在 仿真中， 我們改進 仿真由 節(jié)中的（ 9）式 ，并 在 最佳載體突然變化的情況下比較其與 LMS 算法的性能 [6]。比較聯(lián)合濾波器性能 ,以組成特定的組合。 它 引用 了未知損壞不相關 零均值高斯噪聲，其中 2n? = ， SNR = 15 dB, κ = . 在最初的 30 次迭代的方差估計根據(jù) 式 （ 7）和 CA 的 加權來計算 LMS 的系數(shù) μ。加權系數(shù)的計算 并未使 并行算法增加計算時間，因為它是由硬件實現(xiàn)并行執(zhí)行 的 ，從而增加 了 硬件要求。 元素的集合 Q 中 最小的數(shù) L= 2。 第 2 步 ： 估計 每個 算法 的方差 2q? 。 現(xiàn)在分析最小均方與一些基于相同類型的算法相結合的自適應濾波器，但參數(shù) q 是不同的。對于自適應濾波器，它被賦值， [3]： 組合 自適應濾波器 合并后的自適應濾波器的基本思想是在兩個或兩個以上自適應 LMS 算法并? ? ? ?? ? ? ?? ? ? ? ? ?? ?? ?? ?? ? ? ?kkWbi as kWEkWkWkWEkViiiiiii ??? ???? *? ?kTkk VVEM S D ??? lim 行實現(xiàn)與每個迭代之間的最佳選擇， [9]。在非平穩(wěn)情況下，未知系統(tǒng)參數(shù) （即 *kW 最佳載體） 是隨 時間 變化的 。 這種方法可以適用于所有的 LMS 的算法，雖然我們在這里只考慮其中幾個。最常用的自適應系統(tǒng)對那些基于最小均方（ LMS）自適應算法及其 改進 （ 基于 LMS 的 算法）。(k) is changed [6, 7]. The considered adaptive ?ltering problem consists in trying to adjust a set of weighting coef?cients so that the system output, kTkk XWy ? , tracks a reference signal, assumed as kkTkk nXWd ?? * ,where kn is a zero mean Gaussian noise with the variance 2n? ,and *kW is the optimal weight vector (Wiener vector). Two cases will be considered: WWk ?* is a constant (stationary case) and *kW is timevarying (nonstationary case). In nonstationary case the unknown system parameters( . the optimal vector *kW )are time variant. It is often assumed that variation of *kW may be modeled as Kkk ZWW ??? ** 1 is the zeromean random perturbation, independent on kX and kn with the autocorrelation matrix ? ? IZZEG ZTkk 2??? .Note that analysis for the stationary case directly follows for 02?Z? .The weighting coef?cient vector converges to the Wiener one, if the condition from [1, 2] is satis?ed. De?ne the weighting coef?cientsmisalignment, [1–3], *kkk WWV ?? . It is due to both the effects of gradient noise (weighting coef?cients variations around the average value) and the weighting vector lag (difference between the average and the optimal value), [3]. It can be expressed as: ? ?? ? ? ?? ?*kkkkk WWEWEWV ???? , (2) According to (2), the ith element of kV is: (3) where ? ?? ?kWbias i is the weighting coef?cient bias and ??ki? is a zeromean random variable with the variance 2? .The variance depends on the type of LMSbased algorithm, as well as on the external noise variance 2n? .Thus, if the noise variance is constant or slowlyvarying, 2? is time invariant for a particular ? ? ? ?? ? ? ?? ? ? ? ? ?? ?? ?? ?? ? ? ?kkWbi as kWEkWkWkWEkViiiiiii ??? ???? * LMSbased algorithm. In that sense, in the analysis that follows we will assume that 2? depends only on the algorithm type, . on its parameters. An important performance measure for an adaptive ?lter is its mean square deviation (MSD) of weighting coef?cients. For the adaptive ?lters, it is given by, [3]: ? ?kTkk VVEM S D ??? lim. 3. Combined adaptive ?lter The basic idea of the bined adaptive ?lter lies in parallel implementation of two or more adaptive LMSbased algorithms, with the choice of the best among them in each iteration [9]. Choice of the most appropriate algorithm, in each iteration, reduces to the choice of the best value for the weighting coef?cients. The best weighting coef?cient is the one that is, at a given instant, the closest to the corresponding value of the Wiener vector. Let ? ?qkWi , be the i ?th weighting coef?cient for LMSbased algorithm with the chosen parameter q at an instant k. Note that one may now treat all the algorithms in a uni?ed way (LMS: q ≡ 181。 various parameters affecting the step for VS LMS). These parameters crucially in?uence the ?lter output during two adaptation phases:transient and steady state. Choice of these parameters is mostly based on some kind of tradeoff between the quality of algorithm performance in the mentioned adaptation phases. We propose a possible approach for the LMSbased adaptive ?lter performance improvement. Namely, we make a bination of several LMSbased FIR ?lters with different parameters, and provide the criterion for choosing the most suitable algorithm for different adaptation phases. This method may be applied to all the LMSbased algorithms, although we here consider only several of them. The paper is organized as follows. An overview of the considered LMSbased algorithms is given in Section 3 proposes the criterion for evaluation and bination of adaptive algorithms. Simulation results are pres