基于线性预测的盲最小均方误差均衡器

盲过采样均衡器仅用二阶统计量便可减小码间干扰,该文采用线性预测方法,提出了一种盲最小均方误差(MMSE)均衡器。该方法不需要先估计信道,可直接利用过采样的接收信号均衡信道。此外,该均衡器可采用递推最小二乘算法自适应地实现,具有较高的计算效率。仿真结果表明,该均衡器比基于线性预测的盲置零均衡器有更小的符号估计均方误差。     

基于RENYI熵的水声信道判决反馈盲均衡算法研究

在水下通信系统中,为了抑制由多径效应产生的严重码间干扰,必须进行信道均衡。针对传统的常数模判决反馈盲均衡(CMA-DFE)收敛速度较慢的问题,该文提出了一种基于RENYI熵的判决反馈盲均衡算法(RENYI-DFE)。该算法使用RENYI熵算法调节均衡器前向权向量,用CMA算法调节均衡器反馈权向量,与CMA-DFE相比,该算法在计算量增加很小的情况下,使得盲均衡算法的收敛速度显著增加。仿真结果证明了该算法的优越性。     

一种用于数字QAM接收机的盲均衡器实现

提出了一种适用于数字QAM接收机的自适应盲均衡器实现方案。该均衡器采用多模算法(MMA)和最小均方算法(LMS)，称为MMA—LMS算法结合判决反馈结构(DFE)，即采用前向滤波器和反馈滤波器两级滤波器组实现，提高了信道的适应性能和降低均衡器的阶数。仿真结果表明，该均衡器比一般采用恒模算法(CMA)的横式均衡器有更好的性能，更易于硬件实现。     

一种基于盲均衡的新均衡器算法及实现

基于简化星座图(RCA)的盲均衡算法提出了一种新的均衡器算法———RCA-LMS算法,有效地结合了RCA算法和传统的最小均方(LMS)算法的优点,并应用于选择性反馈均衡器(selective feedback equalizer,SFE)结构,实现了RCA-LMS选择性反馈均衡器(RCA-LMSSFE)。仿真结果表明,该均衡器在低信噪比条件下,能较快地收敛,有较小的稳态剩余误差,从而有效地减小了信号的码间干扰,提高了通信系统的抗干扰能力。     

基于DD-LMS和MCMA的盲判决反馈均衡算法研究

文章对具有DFE结构的盲均衡算法作了研究,在一种修正常模算法(MCMA)代价函数中引入泄漏因子,并将常模算法(CMA)和直接判决-最小均方误差算法(DD-LMS)同时应用到盲判决反馈均衡器的抽头更新中,得到一种适用范围广?均衡特性好?变步长的DD-LLMS MCMBDFE算法。该算法在均衡的同时能自动补偿由信道引起的相位误差,收敛速度快,收敛后剩余误差小,同时还能克服当均衡器长期没有持续输入激励时,LMS算法产生的抽头系数漂移问题。仿真结果表明DD-LLMS MCMBDFE算法是一种有效的盲判决反馈均衡算法。     

基于输出信号峰度的时变步长恒模盲均衡算法的研究

本文以均衡器的输出信号峰度作为控制步长的参量,提出了一种新的时变步长恒模(CMA)盲均衡算法.理论分析和计算机仿真均表明该算法与传统的恒模算法相比,具有更快的收敛速度和更小的稳态剩余误差.     

瑞利衰落电离层信道的自适应盲均衡

研究通过具有加性高斯白噪声的瑞利慢衰落电离层信道的恒模盲均衡技术。著名的恒模算法(CMA)是一种重要的盲信道均衡方法。但是，普通恒模算法存在收敛速度慢和相位旋转等不足。采用一种修正恒模算法(MCMA)，该算法使修正的误差函数最小并且自适应学习率由接收序列即时调整。两种算法用8—PSK信号进行了盲均衡比较，模拟结果显示修正恒模算法比普通恒模算法的收敛速度快，符号间干扰(ISI)小，另外该算法均衡器输出具有既无相位旋转也无延迟等优点。     

稀疏水声信道判决反馈盲均衡算法研究

针对高速水声通信中信道的稀疏特性,提出了一种基于常数模准则的稀疏水声信道判决反馈盲均衡算法。该算法将改进的常数模算法与一种变化的判决反馈均衡器结构(部分反馈均衡器)有机结合,利用水声信道的稀疏特性,不但很好地实现了稀疏水声信道的盲均衡,而且简化了计算,易于算法的硬件实现。用典型稀疏水声信道进行了计算机仿真。结果表明,该算法性能稳定,计算量小,稳态均方误差低,整体性能与基于自适应LMS的稀疏迭代算法接近。该研究为高速水声通信中稀疏信道的均衡提供了一种可实现的方法。     

基于QR-RLS算法的OFDM系统盲自适应时域均衡器

在正交频分复用(OFDM)系统中,使用时域均衡器来消除由于循环前缀长度小于信道时延扩展长度而导致的符号间干扰。为了克服Merry算法收敛速度较慢的缺点,提出了一种适用于无线时变信道环境的改进的盲自适应时域均衡器。该算法利用QR-RLS算法实现均衡器抽头的迭代计算,改善了Merry算法的收敛速度和鲁棒性。理论分析和仿真结果表明,该算法收敛速度明显优于Merry算法,且性能接近MSSNR算法最优解。     

自适应均衡器的FPGA设计

论文介绍了自适应盲均衡器的FPGA设计,主要对自适应均衡器的核心运算单元-采用booth编码算法设计的高性能乘累加(MAC)运算单元进行了详细描述。     

基于最小二乘支持向量机的短期负荷预测模型

支持向量机（SVM）是近年来发展起来的机器学习的新方法,它较好地解决了小样本、非线性、高维数、局部极小点等实际问题。研究了支持向量机的拓展算法——最小二乘支持向量机（LSSVM）,并将其应用于电力系统短期负荷时间序列预测。通过实例并与神经网络模型预测结果相比较表明,LSSVM模型的预测精度要明显高于神经网络模型,验证了LSS-VM模型可以很好地应用于短期负荷时间序列预测,并且具有较高的准确性与有效性,这为短期负荷预测提供了一个新的解决思路。     

Blind equalizer for constant-modulus signals based on Gaussian process regression

A new blind equalization method for constant modulus (CM) signals based on Gaussian process for regression (GPR) by incorporating a constant modulus algorithm (CMA)-like error function into the conventional GPR framework is proposed. The GPR framework formulates the posterior density function for weights using Bayes' rule under the assumption of Gaussian prior for weights. The proposed blind GPR equalizer is based on linear-in-weights regression model, which has a form of nonlinear minimum mean-square error solution. Simulation results in linear and nonlinear channels are presented in comparison with the state-of-the-art support vector machine (SVM) and relevance vector machine (RVM) based blind equalizers. The simulation results show that the proposed blind GPR equalizer without cumbersome cross-validation procedures shows the similar performances to the blind SVM and RVM equalizers in terms of intersymbol interference and bit error rate.     

基于改进LSSVM的城市轨道交通供电故障诊断方法

小样本条件下供电系统故障快速诊断是保证城市轨道交通安全稳定运行的保证.文中提出了一种基于量子粒子群优化最小二乘支持向量机(LSSVM)的供电系统故障诊断方法.该方法首先基于主成分分析提取能够表征系统运行状态的特征参数,并降低数据维数.然后利用LSSVM构建小样本故障诊断模型,通过量子粒子群算法对LSSVM模型参数进行优...     

Support vector machine techniques for nonlinear equalization

The emerging machine learning technique called support vector machines is proposed as a method for performing nonlinear equalization in communication systems. The support vector machine has the advantage that a smaller number of parameters for the model can be identified in a manner that does not require the extent of prior information or heuristic assumptions that some previous techniques require. Furthermore, the optimization method of a support vector machine is quadratic programming, which is a well-studied and understood mathematical programming technique. Support vector machine simulations are carried out on nonlinear problems previously studied by other researchers using neural networks. This allows initial comparison against other techniques to determine the feasibility of using the proposed method for nonlinear detection. Results show that support vector machines perform as well as neural networks on the nonlinear problems investigated. A method is then proposed to introduce decision feedback processing to support vector machines to address the fact that intersymbol interference (ISI) data generates input vectors having temporal correlation, whereas a standard support vector machine assumes independent input vectors. Presenting the problem from the viewpoint of the pattern space illustrates the utility of a bank of support vector machines. This approach yields a nonlinear processing method that is somewhat different than the nonlinear decision feedback method whereby the linear feedback filter of the decision feedback equalizer is replaced by a Volterra filter. A simulation using a linear system shows that the proposed method performs equally to a conventional decision feedback equalizer for this problem     

基于改进型鲸鱼优化算法和最小二乘支持向量机的炼钢终点预测模型研究

终点碳含量是决定钢质量的关键因素,是转炉炼钢过程中需要控制的核心变量之一.本文建立了一种基于莱维飞行的鲸鱼优化算法（Levy Whale Optimization Algorithm,LWOA）和最小二乘向量机（Least Squares Support Vector Machine,LSSVM）的钢水终点碳含量综合预测模型.通过莱维飞行代替了传统鲸鱼优化算法（Whale Optimization Algorithm,WOA）参数的随机选择,优化了鲸鱼算法中跳出局部最优的能力;借助改变鲸鱼算法的系数向量收敛方式明显提高了鲸鱼优化算法的泛化能力、预测精度和收敛速度.数据仿真结果表明,所提出的LWOA-LSSVM预测模型,不仅能够克服局部寻优获取全局最优解,而且具有快速的收敛速度和更高的预测精度,得出预测结果的均方根误差、平均绝对误差和平均绝对百分比误差与遗传算法BP神经网络、遗传算法最小二乘支持向量机和传统鲸鱼算法最小二乘支持向量机相比均有着明显提高.同时,通过调整目标命中率和训练输入样本量验证了预测模型具有更好的鲁棒性.     

基于小波包和支持向量机的故障诊断方法研究

基于支持向量机（SVM,support vector machine）对小样本决策具有较好的学习推广性,本文提出一种基于小波包和支持向量机的故障诊断方法,通过小波包分解系数求取频带能量,并根据各个频带的能量的变化提取故障特征,应用LSSVM（least squares support vector machines）进行故障分类。实验结果表明,支持向量机分类器优于传统的BP神经网络和RBF神经网络分类器,识别率较高,具有更强的泛化推广能力。     

人工鱼群优化最小二乘支持向量机的视频字幕定位

征选择是视频字幕定位的关键,为了提高视频字幕定位正确率,提出一种人工鱼群算法(AFSA)和最小二乘支持向量机(LSSVM)相融合的视频字幕定位模型(AFSA-LSSVM)。首先提取视频字幕特征,然后通过模拟鱼群的觅食、聚群及追尾行为找到最优视频字幕特征子集,最后采用LSSVM建立最优视频字幕定位模型,并进行仿真对比实验。结果表明,相对其它视频字幕定位模型,AFSA-LSSVM提高了视频字幕定位正确率和效率,可为后续视频内容的安全分析提供技术支持.     

Network traffic prediction method based on wavelet transform and multiple models fusion

Accurate prediction of network traffic is an important premise in network management and congestion control. In order to improve the prediction accuracy of network traffic, a prediction method based on wavelet transform and multiple models fusion is presented. Mallat wavelet transform algorithm is used to decompose and reconstruct the network traffic time series. The approximate and detailed components of the original network traffic can be obtained. The characteristics of approximate components and detail components are analyzed by Hurst exponent. Then, according to the different characteristics of the components, autoregressive integrated moving average model (ARIMA) is chosen as the prediction model for the approximate component. Least squares support vector machine (LSSVM) is used to predict detail component. Meanwhile, an improved particle swarm optimization (PSO) algorithm is proposed to optimize the parameters of the LSSVM model. Gauss‐Markov estimation algorithm is adapted to fuse the predicted values of multiple prediction models. The variance of fusion prediction error is smaller than that of single prediction model, and the prediction accuracy is improved. Two actual datasets of network traffic are studied. Compared with other state‐of‐the‐art models, the case study results indicate that the proposed prediction method has a better prediction effect.     

Blind equalization algorithm based on complex support vector regression

A new blind equalization algorithm for complex valued signals was proposed based on the framework of complex support vector regression(CSVR).In the proposed algorithm,the error function of multi-modulus algorithm (MMA) was substituted into CSVR to construct the cost function,and the regression relationship was established by widely linear estimation,and the equalizer coefficients were determined by the iterative re-weighted least square (IRWLS) method.Different from spliting the complex valued signals into real valued signals used in support vector regression,the Wirtinger’s calculus was used in complex support vector regression to analyze the complex signals directly in the complex regenerative kernel Hilbert space.Simulation experiments show that for QPSK modulated signals,compared with the blind equalization algorithm based on support vector regression,the equalization performance of the proposed algorithm is significantly improved in linear channel and nonlinear channel by choosing appropriate kernel function and iterative optimization method.     

基于LSSVM和多数投票原则的遥感影像道路提取

由于高分辨率遥感影像中道路信息的复杂性及其与其他地物光谱信息的相似性,导致基于像素道路提取方法中的道路特征利用不够充分.针对这个问题,对原始影像进行HIS变换提取光谱饱和度(Saturation,S)分量,运用基于区域邻接图(Region Adjacency Graph,RAG)的优化分割算法,实现道路区域的分割,然后利用基于最小二乘支持向量机(LSSVM)和多数投票原则对道路信息进行提取.实验表明,上述方法能有效地提取遥感影像中的道路信息.