共查询到19条相似文献，搜索用时 196 毫秒
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分析了自适应匹配滤波器和向量自回归(VAR)时域白化滤波器.结果表明,通过最小化用误差平方之和估计的均方误差得到的参量滤波器系数和通过相同阶数的多通道最小二乘法得到的VAR滤波器系数是等价的.此外,还分析了VAR滤波器最小二乘估计器的渐进性能,分析了滤波器的运算量和杂波抑制性能. 相似文献
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麦克风阵列具有空间选择特性与高信号增益特性,因而成为非手持式智能语音处理系统中捕捉说话人语音的重要手段。分析了两种典型的自适应算法：最小均方算法（LMS）和递归最小二次方算法（RLS）在麦克风阵列语音增强中的应用,并根据仿真的结果得出结论。 相似文献
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回归系数一类线性估计的小样本性质 总被引：1，自引：0，他引：1
提出了线性模型中回归系数的一类线性估计.在均方误差矩阵(MSEM)准则和Pitman Closeness(PC)准则下,研究了这类线性估计相对于最小二乘(LS)估计的优良性.最后,讨论了当设计阵为非列满秩时,回归系数的可估函数的一类线性估计的优良性. 相似文献
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如何能够更好的提高节点定位一直都是WSN研究的重点,本文在DV-HOP定位算法的基础上,从误差精度,锚节点稳定度和反向学习选择-最小二乘估计来对节点定位的3个方面进行改进。首先在定位误差精度中引入混沌优化算法,其次在锚节点处理中引入稳定度概念,最后在未知节点定位中引入反向学习和最小二乘估计。改进后的算法在通信半径和拓扑结构两个方面进行了仿真实验,实验表明,本文算法能够有效的减少计算带来的定位误差,提高定位精度。 相似文献
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文章认为相空间局域线性回归法是电力系统短期负荷预测混沌预测法中广泛使用的方法,在用线性最小二乘法估计局部线性化模型的参数时,往往由于病态的数据矩阵导致估计值对噪声过于敏感而变得不可信.针对这种情况应用最小均方误差准则和最陡下降原理提出了一种基于自适应滤波电力系统短期负荷预测算法,避免了病态矩阵的影响.实验结果表明该算法预测结果稳定、可靠. 相似文献
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提出非齐次等式约束线性回归模型回归系数的一个新的有偏估计,即综合条件岭估计,讨论了综合条件岭估计的性质,在一定的条件下,综合条件岭估计的样本总方差、均方误差、均方误差矩阵均分别小于约束最小二乘估计的相应误差.在综合条件岭估计下,条件岭估计和条件根方估计为其特例,从而统一了条件岭估计和条件根方估计的理论. 相似文献
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针对目前MIMO-OFDM系统信道估计算法运算复杂度高的问题,本文着重研究低复杂度的基于导频的信道估计算法。首先,发送导频信息获得导频子载波处信道参数,然后利用插值算法来恢复出整个信道的信道参数信息,最后采用运算复杂度低的频域分段最小均方误差(SMMSE)算法,并与基于训练序列的LS和MMSE这两种基本的信道估计算法进行了比较。 相似文献
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Qinyao Liu Feng Ding Yan Wang Cheng Wang Tasawar Hayat 《Journal of The Franklin Institute》2018,355(15):7643-7663
This paper focuses on the parameter estimation problem of multivariate output-error autoregressive systems. Based on the decomposition technique and the auxiliary model identification idea, we derive a decomposition based auxiliary model recursive generalized least squares algorithm. The key is to divide the system into two fictitious subsystems, the one including a parameter vector and the other including a parameter matrix, and to estimate the two subsystems using the recursive least squares method, respectively. Compared with the auxiliary model based recursive generalized least squares algorithm, the proposed algorithm has less computational burden. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithms. 相似文献
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Xuehai Wang Feng Ding Ahmed Alsaedi Tasawar Hayat 《Journal of The Franklin Institute》2017,354(1):593-609
This paper surveys the identification of observer canonical state space systems affected by colored noise. By means of the filtering technique, a filtering based recursive generalized extended least squares algorithm is proposed for enhancing the parameter identification accuracy. To ease the computational burden, the filtered regressive model is separated into two fictitious sub-models, and then a filtering based two-stage recursive generalized extended least squares algorithm is developed on the basis of the hierarchical identification. The stochastic martingale theory is applied to analyze the convergence of the proposed algorithms. An experimental example is provided to validate the proposed algorithms. 相似文献
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A semi-blind adaptive space–time equaliser (STE) has recently been proposed based on a concurrent gradient-Newton (GN) constant modulus algorithm (CMA) and soft decision-directed (SDD) scheme for dispersive multiple-input multiple-output (MIMO) systems that employ high-throughput quadrature amplitude modulation signalling. A minimum number of training symbols, approximately equal to the dimension of the STE, is used to provide a rough initial estimate of the STE's weight vector. The concurrent GN based CMA and SDD blind adaptive scheme is then adopted to adapt the STE. This semi-blind STE has a complexity similar to that of the training-based recursive least squares (RLS) algorithm. For stationary MIMO channels, it has been demonstrated that this semi-blind adaptive STE is capable of converging fast to the optimal minimum mean square error STE solution. In this contribution, we investigate the performance of this semi-blind adaptive STE operating in Rayleigh fading MIMO systems. Our results obtained show that the tracking performance of this semi-blind adaptive algorithm is close to that of the training-based RLS algorithm. Thus, this semi-blind adaptive STE offers an effective and practical means to successfully operate under the highly dispersive and fading MIMO environment. 相似文献
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This study presents a novel frequency synchronization scheme for orthogonal frequency division multiple access uplink systems. The proposed approach first estimates the carrier frequency offset (CFO) from the zeros of a backward prediction system. Then, based on the CFO estimates, two types of filters, namely zero-forcing and the linearly constrained minimum variance filters, are developed to suppress multiple access interference (MAI). Computer simulation results show that in addition to having a reduced computational complexity, the proposed algorithm has reliable CFO estimates and possesses at least a 3-dB power gain in MAI suppression over conventional minimum mean square error algorithms for frequency synchronization. 相似文献
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This paper presents a decomposition based least squares estimation algorithm for a feedback nonlinear system with an output error model for the open-loop part by using the auxiliary model identification idea and the hierarchical identification principle and by decomposing a system into two subsystems. Compared with the auxiliary model based recursive least squares algorithm, the proposed algorithm has a smaller computational burden. The simulation results indicate that the proposed algorithm can estimate the parameters of feedback nonlinear systems effectively. 相似文献
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《Journal of The Franklin Institute》2022,359(3):1362-1381
Error entropy is a well-known learning criterion in information theoretic learning (ITL), and it has been successfully applied in robust signal processing and machine learning. To date, many robust learning algorithms have been devised based on the minimum error entropy (MEE) criterion, and the Gaussian kernel function is always utilized as the default kernel function in these algorithms, which is not always the best option. To further improve learning performance, two concepts using a mixture of two Gaussian functions as kernel functions, called mixture error entropy and mixture quantized error entropy, are proposed in this paper. We further propose two new recursive least-squares algorithms based on mixture minimum error entropy (MMEE) and mixture quantized minimum error entropy (MQMEE) optimization criteria. The convergence analysis, steady-state mean-square performance, and computational complexity of the two proposed algorithms are investigated. In addition, the reason why the mixture mechanism (mixture correntropy and mixture error entropy) can improve the performance of adaptive filtering algorithms is explained. Simulation results show that the proposed new recursive least-squares algorithms outperform other RLS-type algorithms, and the practicality of the proposed algorithms is verified by the electro-encephalography application. 相似文献
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In this paper, we consider the parameter estimation issues of a class of multivariate output-error systems. A decomposition based recursive least squares identification method is proposed using the hierarchical identification principle and the auxiliary model idea, and its convergence is analyzed through the stochastic process theory. Compared with the existing results on parameter estimation of multivariate output-error systems, a distinct feature for the proposed algorithm is that such a system is decomposed into several sub-systems with smaller dimensions so that parameters to be identified can be estimated interactively. The analysis shows that the estimation errors converge to zero in mean square under certain conditions. Finally, in order to show the effectiveness of the proposed approach, some numerical simulations are provided. 相似文献
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Two auxiliary model based recursive identification algorithms, a generalized extended stochastic gradient algorithm and a recursive generalized extended least squares algorithm, are developed for multivariable Box–Jenkins systems. The basic idea is to use the auxiliary models to estimate the unknown noise-free outputs of the system and to replace the unmeasurable terms in the information vectors with their estimates. We prove that the estimation errors given by the proposed algorithms converge to zero under the persistent excitation condition. Finally, an example is provided to show the effectiveness of the proposed algorithms. 相似文献
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Feng Ding Huibo Chen Ling Xu Jiyang Dai Qishen Li Tasawar Hayat 《Journal of The Franklin Institute》2018,355(8):3737-3752
Mathematical models are basic for designing controller and system identification is the theory and methods for establishing the mathematical models of practical systems. This paper considers the parameter identification for Hammerstein controlled autoregressive systems. Using the key term separation technique to express the system output as a linear combination of the system parameters, the system is decomposed into several subsystems with fewer variables, and then a hierarchical least squares (HLS) algorithm is developed for estimating all parameters involving in the subsystems. The HLS algorithm requires less computation than the recursive least squares algorithm. The computational efficiency comparison and simulation results both confirm the effectiveness of the proposed algorithms. 相似文献