共查询到20条相似文献,搜索用时 981 毫秒
1.
The nonlinear Wiener stochastic gradient adaptive algorithm for third-order Volterra system identification application with Gaussian input signals is presented. The complete self-orthogonalisation procedure is based on the delay-line structure of the nonlinear discrete Wiener model. The approach diagonalises the autocorrelation matrix of an adaptive filter input vector which dramatically reduces the eigenvalue spread and results in more rapid convergence speed. The relationship between the autocorrelation matrix and cross-correlation matrix of filter input vectors of both nonlinear Wiener and Volterra models is derived. The algorithm has a computational complexity of O(M/sup 3/) multiplications per sample input where M represents the length of memory for the system model, which is comparable to the existing algorithms. It is also worth noting that the proposed algorithm provides a general solution for the Volterra system identification application. Computer simulations are included to verify the theory. 相似文献
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A new adaptive Volterra filter with fast convergence is proposed, where the M-band discrete wavelet transform and Gram-Schmidt orthogonalisation are utilised. In particular, the cascade combination of two such procedures reduces the eigenvalue spread of the Volterra input auto-correlation matrix, thus improving the convergence speed of the adaptive nonlinear filtering 相似文献
3.
研究输入、输出观测数据均受噪声干扰时的非线性Volterra系统的全解耦自适应滤波问题.基于总体最小二乘技术和Volterra滤波器的伪线性组合结构,运用约束优化问题的分析方法研究Volterra滤波过程,从而建立了一种总体全解耦自适应滤波算法.并建立了分析该算法收敛性能的参数反馈调整模型,分析表明,该算法可使各阶Volterra核稳定地收敛到真值.仿真实验的结果表明,当输入、输出观测数据均受噪声干扰时,总体全解耦自适应滤波算法的鲁棒抗噪性能和滤波精度均优于全解耦LMS自适应滤波算法. 相似文献
4.
A new RLS adaptive Volterra filter is presented. The nonlinear filtering problem is transformed into an equivalent multichannel, but linear, filtering problem. The multichannel input signal is completely orthogonalised using sequential processing multichannel lattice stages. With the complete orthogonalisation, the filter becomes simple, highly modular and suitable for VLSI implementations 相似文献
5.
Resende L.S. Romano J.M.T. Bellanger M.G. 《Signal Processing, IEEE Transactions on》2004,52(3):636-644
This paper proposes a new structure for split transversal filtering and introduces the optimum split Wiener filter. The approach consists of combining the idea of split filtering with a linearly constrained optimization scheme. Furthermore, a continued split procedure, which leads to a multisplit filter structure, is considered. It is shown that the multisplit transform is not an input whitening transformation. Instead, it increases the diagonalization factor of the input signal correlation matrix without affecting its eigenvalue spread. A power normalized, time-varying step-size least mean square (LMS) algorithm, which exploits the nature of the transformed input correlation matrix, is proposed for updating the adaptive filter coefficients. The multisplit approach is extended to linear-phase adaptive filtering and linear prediction. The optimum symmetric and antisymmetric linear-phase Wiener filters are presented. Simulation results enable us to evaluate the performance of the multisplit LMS algorithm. 相似文献
6.
van Veen B.D. Leblond O. Mani V.P. Sebald D.J. 《Signal Processing, IEEE Transactions on》2000,48(4):1076-1085
An algorithm for multi-input multi-output (MIMO) adaptive filtering is introduced that distributes the adaptive computation over a set of linearly connected computational modules. Each module has an input and an output and transmits data to and receives data from its nearest neighbor. A gradient-based algorithm for adapting the parameters in each module to minimize the global mean-squared error is derived using principles of back propagation. The performance surface is explored to understand the characteristics of the adaptive algorithm. The minimum mean-squared error is a many to one function of the parameters; therefore, upper bounds on each parameter are used to prevent excessive parameter drift and ensure stability with fixed step sizes. Guidelines for choosing the LMS algorithm step sizes and initial conditions are developed. Several examples illustrate the performance of the algorithm 相似文献
7.
二阶Volterra数据块LMS算法利用当前时刻及其以前时刻更多输入信号和误差信号的信息提高了算法的收敛速度,但由于其固定数据块长取值的不同导致了算法的收敛速度和稳态误差此消彼长。针对这个问题,本文提出一种二阶Volterra变数据块长LMS算法,通过时刻改变输入信号数据块长度提高算法性能。本算法首先采用两个并行的二阶Volterra滤波器,其输入信号数据块长差值始终保持一个单位;然后将其各自的输出误差信号同时输入到数据块长判决器,通过判决器得到下一时刻各个滤波器输入信号的数据块长度;最后以第1个二阶Volterra滤波器的输出作为整个滤波系统的输出,从而改善了算法性能。将本算法应用于非线性系统辨识,计算机仿真结果表明,高斯噪声背景下本算法的收敛速度和稳态性能都得到了明显的提高。 相似文献
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本文使用Hammerstein模型和维纳模型代替Volterra级数模型来模拟非线性结构以降低运算复杂度,提出了一个由Hammerstein模型和维纳模型构建成的非线性信道传输系统的模型,由此模型给出并推导出了基于该信道模型的NCLMS算法、改进1型NCLMS Newton算法和改进2型NCLMS Newton算法.仿... 相似文献
10.
本文对基于子带分解的自适应滤波做了研究,给出子带分解下的包含子带间滤波的最优维纳解和LMS算法,并分析了其收敛性能和计算复杂度,与传统的LMS算法相比,基于子带分解的自适应滤波具有更好的性能,计算机模拟结果也体现了这一点。 相似文献
11.
Adaptive polynomial filters 总被引:1,自引:0,他引:1
Adaptive nonlinear filters equipped with polynomial models of nonlinearity are explained. The polynomial systems considered are those nonlinear systems whose output signals can be related to the input signals through a truncated Volterra series expansion or a recursive nonlinear difference equation. The Volterra series expansion can model a large class of nonlinear systems and is attractive in adaptive filtering applications because the expansion is a linear combination of nonlinear functions of the input signal. The basic ideas behind the development of gradient and recursive least-squares adaptive Volterra filters are first discussed. Adaptive algorithms using system models involving recursive nonlinear difference equations are then treated. Such systems may be able to approximate many nonlinear systems with great parsimony in the use of coefficients. Also discussed are current research trends and new results and problem areas associated with these nonlinear filters. A lattice structure for polynomial models is described 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
14.
DeBrunner V.E. Dayong Zhou 《IEEE transactions on circuits and systems. I, Regular papers》2006,53(3):653-661
The filtered-error LMS (FELMS) algorithms are widely used in multi-input and multi-output control (MIMO) active noise control (ANC) systems as an alternative to the filtered-x LMS (FXLMS) algorithms to reduce the computational complexity and memory requirements. However, the available FELMS algorithms introduce significant delays in updating the adaptive filter coefficients that slow the convergence rate. In this paper, we introduce a novel algorithm called the hybrid filtered-error LMS algorithm (HFELMS) which, while still a form of the FELMS algorithm, allows users to have some freedom to construct the error filter that guarantees its convergence with a sufficiently small step size. Without increasing the computational complexity, the proposed algorithm can improve the control system performance in one of several ways: 1) increasing the convergence rate without extra computation cost; 2) reducing the remaining noise mean square error (MSE); or 3) shaping the excess noise power. Simulation results show the effectiveness of the proposed method. 相似文献
15.
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
16.
Nonlinear effects in LMS adaptive equalizers 总被引:1,自引:0,他引:1
An adaptive transversal equalizer based on the least-mean-square (LMS) algorithm, operating in an environment with a temporally correlated interference, can exhibit better steady-state mean-square-error (MSE) performance than the corresponding Wiener filter. This phenomenon is a result of the nonlinear nature of the LMS algorithm and is obscured by traditional analysis approaches that utilize the independence assumption (current filter weight vector assumed to be statistically independent of the current data vector). To analyze this equalizer problem, we use a transfer function approach to develop approximate analytical expressions of the LMS MSE for sinusoidal and autoregressive interference processes. We demonstrate that the degree to which LMS may outperform the corresponding Wiener filter is dependent on system parameters such as signal-to-noise ratio (SNR), signal-to-interference ratio (SIR), equalizer length, and the step-size parameter 相似文献
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Efficient algorithms for Volterra system identification 总被引:1,自引:0,他引:1
Glentis G.-O.A. Koukoulas P. Kalouptsidis N. 《Signal Processing, IEEE Transactions on》1999,47(11):3042-3057
In this paper, nonlinear filtering and identification based on finite-support Volterra models are considered. The Volterra kernels are estimated via input-output statistics or directly in terms of input-output data. It is shown that the normal equations for a finite-support Volterra system excited by zero mean Gaussian input have a unique solution if, and only if, the power spectral process of the input signal is nonzero at least at m distinct frequencies, where m is the memory of the system. A multichannel embedding approach is introduced. A set of primary signals defined in terms of the input signal serve to map efficiently the nonlinear process to an equivalent multichannel format. Efficient algorithms for the estimation of the Volterra parameters are derived for batch, as well as for adaptive processing. An efficient order-recursive method is presented for the determination of the Volterra model structure. The proposed methods are illustrated by simulations 相似文献
19.
A new fast algorithm for multichannel linear and quadratic adaptive filtering using the Chandrasekhar equations is presented. Based on the shift-invariance property, the multichannel linear model could be described by a time-invariant state-space model to which we apply the Chandrasekhar factorization technique, which provides interesting numerical properties. Furthermore, a new method for nonlinear filtering is given where the multichannel Chandrasekhar algorithm is applied on the second-order Volterra (SOV) filter after suitable transformations 相似文献
20.
在非线性网络响应分析中,采用Volterra级数法可以导出与线性系统传递函数相似的非线性传递函数,能使非线性系统用线性化和系统化方法达到精确分析.文中给出了非线性网络响应的Volterra级数解的连续算式,为解决连续算式计算麻烦的问题,提出用方波脉冲技术处理用Volterra级数表示法描述的非线性网络响应与激励之间关系的一组广义卷积积分的迭加计算,从而得到非线性网络响应求解的Volterra级数解的离散算式.仿真表明该算法求出的非线性网络响应与真实模型曲线十分逼近,证明了它的有效性. 相似文献