基于正交小波包变换的前馈神经网络盲均衡算法

针对前馈神经网络盲均衡算法收敛速度慢、均方误差大的缺点,在分析前馈神经网络盲均衡算法和正交小波包变换理论的基础上,提出了基于正交小波包变换的前馈神经网络盲均衡算法。该算法利用正交小波包变换良好的去相关性,对前馈神经网络均衡器输入信号进行预处理后,降低了输入信号的自相性,从而加快了收敛速度和减小了均方误差。水声信道的仿真结果表明,该算法在收敛速度与均方误差方面的性能比前馈神经网络盲均衡算法优越。     

一种MMSE类盲均衡算法的凸组合策略

通常有2类重要的MMSE类盲均衡算法：恒模盲均衡算法和判决引导算法。前者面临收敛速度慢,剩余误差大的缺点;而判决引导均衡算法的收敛速度快,稳态性能好,缺点是启动条件不满足时算法会发散。本论文提出了一种MMSE类盲均衡算法的凸组合策略。通过应用凸组合的基本原理,恒模盲均衡算法可以自适应平滑地切换到判决引导模式。首先,提出了算法的凸组合策略,使得恒模盲均衡算法和判决引导算法之间可以自动软切换。然后描述了一种锚定过程,可以消除稳态时恒模均衡器过高的剩余误差,因此总的算法可以看作是经过了恒模均衡器训练的判决引导均衡。仿真实验证实了新算法的优异性能。     

基于系数查找表的全相位FIR自适应陷波器

本文分析了全相位DFT数字滤波器优良的频域传输特性,进而提出基于滤波器系数抽取查找表的全相位自适应陷波算法.该算法以陷波器输出信号的能量作为判断依据选择抽头系数,省去了传统频域FIR滤波器的FFT步骤,因而计算量小,而且变传统闭环控制为开环控制,因而收敛快,并可把频率设置点上的干扰完全陷波到0.因而相比于传统ⅡR和FIR陷波器,性能有了很大提高.     

高阶QAM实时多域测试多模式自适应盲均衡技术研究

提出了一种全新的宽带通信信号实时多域分析通用架构,详细介绍了该架构下信号分析的基本原理。在这种架构的基础上,通过加载不同的算法,不仅能够实现各种宽带通信信号高精度实时宽带频谱分析,而且还能同时实现宽带通信信号时域、调制域等多域联合分析。针对宽带高阶正交幅度调制（QAM）通信信号实时多域分析,详细讨论了面向测试的基于GMMA和DDLMS双模自适应盲均衡算法。系统仿真结果证明：相比GMMA自适应盲均衡算法,双模自适应盲均衡算法收敛速度明显提高,256QAM信号均衡后输出残余码间串扰（ISI）改善提高了10dB;同时通过实验验证,采用20MHz实时分析带宽对码率为6.4MSps的宽带256QAM信号进行实时多域分析,误差矢量幅度（error vectorm agnitude,EVM）测试误差小于2%。     

一种新的变步长LMS算法

自适应滤波器在信号检测、信号恢复、数字通信等许多领域中被广泛应用,自适应算法一直是学术界一个重要研究课题,提出了一种新的变步长LMS算法.算法根据自适应滤波收敛程度的加深,逐渐减小步长.试验结果表明应用该算法设计的自适应滤波器与当今通用的变步长LMS算法相比具有运算简单,收敛速度更快等优点.     

一种并行双模式盲均衡算法的研究与实现

本文提出了一种将SMMA算法与DD算法并行运行的变步长双模式盲均衡算法。该算法在保持原来两种算法高性能的基础上，改进传统的DD算法，将两种算法采用并行结构运行，同时加入误差控制函数，控制SMMA算法占比，降低稳态误差；并且引入变步长方法，加快算法的收敛速度。经过理论分析与仿真实验表明，该算法相对于传统方法，ISI降低到-29.8 dB，收敛速度提高，在630个符号左右算法完成收敛。在完成仿真实验后，进行了实测信号的解调均衡，其EVM降低到1.69%。     

基于遗传算法和余弦序列的数字滤波器设计

有限长单位脉冲响应(FIR)数字滤波器具有系统稳定、线性相位、实现简单等优点,在数字信号处理领域得到了广泛应用:遗传算法具有记忆性、分布性和多样性等优点,广泛用于钾能计算、模式识别、优化设计等领域.本文提出了基于遗传算法和余弦序列相结合的FIR数字滤波器设计方法,即采用对余弦序列加权求和方式生成窗口函数,而加权系数通过遗传算法求解得到,并采用加窗方法实现数字滤波器.为了提高收敛速度和收敛精度,采用了精华模型和浮点编码等有效手段.以低通数字滤波器为例进行了仿真实验,实验结果验证了设计算法的有效性.本文提出的数字滤波器设计算法具有设计灵活、适应广泛、容易实现等优点.     

非线性协调系统的仪表故障诊断研究

摘要:采用Laguerre(拉格里)滤波器提出了非线性系统仪表故障诊断新方案。由于拉格里滤波器的基函数彼此正交,滤波器参数收敛快而精确,因此采用拉格里滤波器对对象进行在线建模;根据建模的对象对输出进行单步预测;与实际对象输出进行比较以产生残差信号;通过对残差信号的分析对3种常见的仪表故障进行诊断。对某电厂非线性锅炉汽轮机协调系统中的仪表故障诊断进行仿真研究,结果表明该仪表故障诊断方案对参数扰动有很强鲁棒性,结果令人满意。     

基于MATLAB仿真的LMS和LCMV算法抗干扰性能比较研究

对空域抗干扰技术中常用的最小均方（LMS）算法和线性约束最小方差（LCMV）算法的原理进行了说明,建立了多元天线阵列模型,并以此模型为基础,对这两种算法的收敛速度、信噪比和误码率等参数进行了分析和比较,最后采用MATLAB进行了仿真。仿真结果表明,LCMV算法的收敛速度较LMS算法要稍慢,但是达到最终稳定时的误差均值以及收敛效率要优于LMS算法。     

结构自适应滤波技术的研究概述

自适应滤波技术是信号处理学科的一个重要分支,结构自适应滤波又是其进入21世纪后新兴的研究方向。以研究结构自适应滤波技术为目的,主要描述结构自适应滤波技术的算法与性能,从传统的结构自适应滤波算法的原理出发,采用算法对比分析的方式,分析了分段滤波(SF)算法、梯度下降(GD)算法以及分数抽头(FT)算法的优缺点,并结合已有文献给出了可行的新型分数抽头(FT)算法,通过应用实例对比说明新型分数抽头(FT)算法具有更好的稳定性、收敛速度以及信噪比。     

Cellular neural network for fast adaptive equalization

The use of an analogue neural network in the adaptive equalization of time-varying communication channels is proposed. the network is used to compute the coefficients of a linear transversal filter. the settling time decreases as the filter order increases and as the signal-to-noise ratio decreases. Owing to the real-time processing capabilities, the network can be useful when it is of interest to track fast variations, as in radio links. the special properties of the tap input correlation matrix result in a cellular network architecture which greatly simplifies the VLSI implementation. Simulation results are presented which point out very satisfactory performance.     

An improved decision feedback equalization using a priori information

This paper addresses the exhaustive computational complexity of the maximum‐a‐posteriori equalizer and the inefficiency of the conventional decision feedback equalizer (DFE) algorithm in iterative equalization, especially when the higher‐level modulation is used with severely distorted Inter Symbol Interference channels. The new method proposed here improves the bit error rate (BER) performance by computing the extra metric r_n+1 using the feedback symbols from previous iteration and combining it with a priori information of the symbols. After each iteration, the hard‐detected symbols are saved in the memory as a priori data for next iteration. We verified the proposed algorithm for Binary Phase Shift Keying and 8‐phase shift keying modulation. The promising simulation results show that the BER performance given by the proposed low complexity DFE algorithm improved dramatically throughout the iterations when the conventional DFE has only insignificant improvement in the process of iterative equalization. Copyright © 2009 John Wiley & Sons, Ltd.     

Real‐time identification of nonlinear multiple‐input–multiple‐output systems with unknown input time delay using Wiener model with Neuro‐Laguerre structure

In this article, a real‐time block‐oriented identification method for nonlinear multiple‐input–multiple‐output systems with input time delay is proposed. The proposed method uses the Wiener structure, which consists of a linear dynamic block (LDB) followed by a nonlinear static block (NSB). The LDB is described by the Laguerre filter lattice, whereas the NSB is characterized using the neural networks. Due to the online adaptation of the parameters, the proposed method can cope with the changes in the system parameters. Moreover, the convergence and bounded modeling error are shown using the Lyapunov direct method. Four practical case studies show the effectiveness of the proposed algorithm in the open‐loop and closed‐loop identification scenarios. The proposed method is compared with the recently published methods in the literature in terms of the modeling accuracy, parameter initialization, and required information from the system.     

An easy demonstration of the optimum value of the adaptationconstant in the LMS algorithm [FIR filter theory]

The least mean squares (LMS) is the most widely used algorithm among those proposed to adapt the coefficients of an FIR filter in order to minimize the mean-square error (MSE) between its output and the desired signal. Since the introduction of the LMS algorithm, many variants have been proposed to improve its performance. Doubtless, the most popular is the normalized LMS algorithm, which uses a value for the adaptation constant that assures the fastest convergence. This correspondence shows a new demonstration of the algorithm based on a mathematical approach easier than that usually proposed     

Sliding‐window hybrid quasi‐Newton algorithm‐trained MBER equalisers

Nonlinear equalisers based on minimum BER are proposed for the equalisation of nonlinear time‐varying channels. To train the equalisers online, a sliding‐window‐based hybrid quasi‐Newton algorithm is proposed. Switching between sliding‐window stochastic gradient algorithm and sliding‐window quasi‐Newton algorithm makes the new algorithm significantly stabler with a fast convergence rate. Results from extensive simulation tests show that performance of nonlinear equalisers based on minimum BER is better than the equaliser based on minimum mean square error. The proposed algorithm demonstrates high efficiency as well. Copyright © 2013 John Wiley & Sons, Ltd.     

多径干扰下的物联网链路负载均衡控制算法

在多径干扰下的物联网传输链路容易受到多径效应的影响导致链路均衡性不好,物联网数据转发的误码率较高。为了提高多径干扰下的物联网链路负载均衡控制能力,提出了一种基于分数间隔均衡和误比特率反馈调制技术的物联网数据转发负载均衡控制模型,构建多径干扰下的物联网传输链路模型,分析物联网链路的多径特性,采用扩频信道调制方法进行物联网链路的码间干扰滤波,结合分数间隔均衡技术进行链路均衡设计,采用信道的带宽自适应调节模型进行物联网链路的多径抑制,结合误比特率反馈调制解调技术实现物联网链路负载均衡控制,克服信道对于多径干扰产生的多径衰减的影响,提高物联网链路负载均衡性。仿真结果表明,采用该算法进行物联网链路负载均衡控制,数据转发输出的码元保真性较好,误码率较低,在多径干扰信道的链路转发自适应控制性能较好,提高了物联网链路的稳健性和鲁棒性。     

基于Laguerre模型的过热汽温自适应预测PI控制系统

针对火电厂锅炉过热汽温控制的特点,设计1种基于Laguerre模型的自适应预测PI控制器。该预测控制器采用对时延具有良好逼近能力的正交Laguerre函数模型作为预测模型,利用带遗忘因子的最小二乘法在线辨识Laguerre预测模型的系数,以提高系统适应工况变化的能力;滚动优化指标采用比例积分型结构,以提高系统的快速性和鲁棒性。通过对具有严重参数不确定性、扰动以及大迟滞的电厂过热汽温被控对象进行仿真研究,结果表明该方法能够很好地适应对象特性的变化,且控制系统的性能比常规串级控制系统有较大提高。     

Fractionally spaced blind equalization with low‐complexity concurrent constant modulus algorithm and soft decision‐directed scheme

The paper proposes a low‐complexity concurrent constant modulus algorithm (CMA) and soft decision‐directed (SDD) scheme for fractionally spaced blind equalization of high‐order quadrature amplitude modulation channels. We compare our proposed blind equalizer with the recently introduced state‐of‐art concurrent CMA and decision‐directed (DD) scheme. The proposed CMA+SDD blind equalizer is shown to have simpler computational complexity per weight update, faster convergence speed, and slightly improved steady‐state equalization performance, compared with the existing CMA+DD blind equalizer. Copyright © 2004 John Wiley & Sons, Ltd.