支持向量机初始化的常数模盲均衡算法仿真

为了提高常数模盲均衡算法的收敛速度并避免算法收敛至局部极小,提出了一种支持向量机初始化的常数模盲均衡算法.新算法采用一小段初始数据,利用支持向量机,将盲均衡问题转化为全局最优的支持向量回归问题,对盲均衡器的初始权向量进行设定,而后切换至计算量较小的常数模算法.采用浅海水声信道对新算法进行了计算机仿真,结果表明:支持向量机初始化阶段收敛速度快;切换至常数模算法后性能稳定.该算法适合应用于快衰落水声信道中通信数据的实时恢复.     

基于小波核SVM的水声信道盲均衡算法

推导并提出了一种快速收敛的基于小波核支持向量机的恒模盲均衡器(WSVM-CMBE)。该方法以支持向量机为框架,利用信号的恒模特性构造代价函数,采用小波核函数,并自适应调整核函数中的伸缩因子。通过水声信道仿真实验,并与采用高斯核函数的支持向量机恒模盲均衡器(SVM-CMBE)进行比较,结果证明该方法提高了收敛速度。     

基于支持向量机的软测量技术及其应用

支持向量机(SVM)是一种基于结构风险最小化原理,具有很好推广性能的学习算法。讨论了基于最小二乘支持向量机(LS-SVM)的软测量数据建模原理和方法,并将其应用在汽车排放的氮氧化合物NOX软测量中。通过与基于神经网络的软测量方法进行比较,结果显示出SVM的明显的优势,特别是对小样本、非线性、高维数一类软测量问题的建模,提供了一种有效的途径。     

基于LS-SVM的非线性多功能传感器信号重构方法研究

提出了基于最小二乘支持向量机(Least squares support vector machine, LS-SVM)的非线性多功能传感器信号重构方法. 不同于通常采用的经验风险最小化重构方法, 支持向量机(Support vector machine, SVM)是基于结构风险最小化准则的新型机器学习方法, 适用于小样本标定数据情况, 可有效抑制过拟合问题并改善泛化性能. 在SVM基础上, LS-SVM将不等式约束转化为等式约束, 极大地简化了二次规划问题的求解. 研究中通过L-折交叉验证实现调整参数优化, 在两种非线性情况下对多功能传感器的输入信号进行了重构, 实验结果显示重构精度分别达到0.154\%和1.146\%, 表明提出的LS-SVM重构方法具有高可靠性和稳定性, 验证了方法的有效性.     

基于LSSVM的VVP推力系数预报

针对神经网络存在结构较难确定、训练易陷入局部最优以及容易过学习等问题和标准SVM训练速度较慢等问题,提出最小二乘支持向量机算法,最小二乘支持向量机算法(LS-SVM)具有比其他非线性函数逼近方法具有更强的泛化能力;并且LS-SVM采用径向基核函数,得到LSSVM模型的待定参数比标准支持向量机少,仅为2个.将最小二乘支持...     

基于LS-SVM及嵌入式技术的力敏传感器温度补偿

将最小二乘支持向量机(LS-SVM)融合改进模拟退火算法(SA)移植于嵌入式智能仪表中,结合嵌入式技术实现了对力敏传感器的温度补偿.由LS-SVM构建力敏传感器的非线性模型,利用改进的模拟退火算法对LS-SVM中的正则化参数和核宽度进行全局寻优,并通过设计嵌入式软硬件平台对该方法进行了验证.试验结果表明,该方法具有易实现,补偿精度较高等特点,对基于嵌入式智能仪表的传感器温度补偿有一定的实际意义.     

水声信道盲均衡优化仿真研究

研究水声信道盲均衡问题,由于水声受码间干扰,信号产生畸变,通信质量低.采用单一水声信道盲均衡算法寻优能力差,不能满足要求,并易获得局部最优解,导致信道盲均衡效果差.为解决上述问题,提出一种水声信道盲均衡组合算法(MPSO-ACO).首先初始化小波盲均衡器的权向量,然后采用动量粒子算法找到权向量次优解集;最后采用蚁群算法对次优解集进行局部搜索,找到小波盲均衡器的权向量最优解,从而实现水声信道盲均衡.仿真结果表明,相对于传统水声信道盲均衡算,MPSO-ACO算法不仅降低了误码率,而且加快了收敛速度,获得了更优水声信道盲均衡效果.     

惯性导航系统非线性初始对准的LS-SVM方法研究

由陀螺仪和加速度计等惯性传感器组成的惯性导航系统,在进入导航状态之前必须进行初始对准.根据支持向量机强大的非线性映射能力,建立了基于最小二乘支持向量机(LS-SVM)的捷联惯导系统非线性初始对准方法.LS-SVM避开了经典支持向量机求解时的复杂优化运算,通过求解一组线性方程组就可以得到唯一的全局最优解,所以算法的复杂度大大降低,能更好的满足工程应用中的实时性要求.针对方位初始失准角为大角度时的捷联惯导系统非线性误差模型进行仿真分析,并在相同条件下与卡尔曼滤波方法作比较,结果表明LS-SVM在初始对准中的有效性和可行性.     

基于LS-SVM的软测量模型及其工业应用

最小二乘支持向量机(LS-SVM)是支持向量机(SVM)的一种扩展,其算法简练,计算速度快;利用LS-SVM进行特征提取,可以有效地降低输入样本维数,缩减模型的运算时间,同时LS-SVM又具有优越的非线性回归能力;为实现氧化铝高压溶出过程中苛性比值在线测量,建立了一种基于LS-SVM的软测量模型,并将此模型应用于实际生产;工业数据的仿真结果表明该模型具有较高的预测精度和范化能力,能满足在线检测、实时控制的要求。     

基于粒子群优化的非线性系统最小二乘支持向量机预测控制方法

对于非线性系统预测控制问题, 本文提出了一种基于模型学习和粒子群优化(PSO)的单步预测控制算法.该方法使用最小二乘支持向量机(LS-SVM)建立非线性系统模型并预测系统的输出值, 通过输出反馈和偏差校正减少预测误差, 由PSO滚动优化获得非线性系统的控制量. 该方法能在非线性系统数学模型未知的情况下设计出有效的预测控制器. 通过对单变量多变量非线性系统进行仿真, 证明了该预测控制方法是有效的, 且具有良好的自适应能力和鲁棒性.     

Hybrid simplex genetic algorithm for blind equalization using RBF networks

The purpose of this paper is to derive a hybrid simplex genetic algorithm for nonlinear channel blind equalization using RBF networks. Most of the algorithms for blind equalization are focused on linear channel models because of their simplicity. However, most practical channels are better approximated by nonlinear models. In order to find an effective method for nonlinear channel blind equalization, here, the equalizer based on RBF networks which is constructed from channel output states instead of the channel parameters is considered. Using the Bayesian likelihood cost function defined as the accumulation of the natural logarithm of the Bayesian decision variable, the problem becomes to maximize the Bayesian likelihood cost function with the dataset which composes the RBF equalizer’s center. For this high dimensional complex optimal problem, the proposed hybrid simplex genetic algorithm solves it by incorporating the simplex operator with GA, and obtains a good convergence characteristic and satisfied equalization result.     

基于小波包变换的非线性信道均衡器

针对严重线性失真和轻度非线性失真的数字信道，为了提高基于最小均方误差算法的判决反馈均衡器的收敛速度，首先提出用一族正交小波包基函数来表示非线性信道判决反馈均衡器厦其输出，然后给出基于小渡包变换的非线性信道自适应均衡算法。该算法实现了小波包与非线性信道模型的结合，在计算量增加不多的前提下，利用小波包对小波空间的进一步划分以厦比小波变换更强的去相关能力来减小输入信号相关阵的条件数。对典型非线性信道模型的仿真结果表明，该算法可有效提高均衡器的收敛速度。     

Nonlinear channel equalization for QAM signal constellation usingartificial neural networks

Application of artificial neural networks (ANN's) to adaptive channel equalization in a digital communication system with 4-QAM signal constellation is reported in this paper. A novel computationally efficient single layer functional link ANN (FLANN) is proposed for this purpose. This network has a simple structure in which the nonlinearity is introduced by functional expansion of the input pattern by trigonometric polynomials. Because of input pattern enhancement, the FLANN is capable of forming arbitrarily nonlinear decision boundaries and can perform complex pattern classification tasks. Considering channel equalization as a nonlinear classification problem, the FLANN has been utilized for nonlinear channel equalization. The performance of the FLANN is compared with two other ANN structures [a multilayer perceptron (MLP) and a polynomial perceptron network (PPN)] along with a conventional linear LMS-based equalizer for different linear and nonlinear channel models. The effect of eigenvalue ratio (EVR) of input correlation matrix on the equalizer performance has been studied. The comparison of computational complexity involved for the three ANN structures is also provided.     

A second-order blind equalization method robust to ill-conditioned SIMO FIR channels

This paper deals with blind equalization of single-input–multiple-output (SIMO) finite-impulse-response (FIR) channels driven by i.i.d. signal, by exploiting the second-order statistics (SOS) of the channel outputs. Usually, SOS-based blind equalization is carried out via two stages. In Stage 1, the SIMO FIR channel is estimated using a blind identification method, such as the recently developed truncated transfer matrix (TTM) method. In Stage 2, an equalizer is derived from the estimate of the channel to recover the source signal. However, this type of two-stage approach does not give satisfactory blind equalization result if the channel is ill-conditioned, which is often encountered in practical applications. In this paper, we first show that the TTM method does not work in some situations. Then, we propose a novel SOS-based blind equalization method which can directly estimate the equalizer without knowing the channel impulse responses. The proposed method can obtain the desired equalizer even in the case that the channel is ill-conditioned. The performance of our method is illustrated by numerical simulations and compared with four benchmark methods.     

Nonlinear magnetic storage channel equalization using minimalresource allocation network (MRAN)

This letter presents the application of the recently developed minimal radial basis function neural network called minimal resource allocation network (MRAN) for equalization in highly nonlinear magnetic data storage channels. Using a realistic magnetic channel model, MRAN equalizer's performance is compared with the nonlinear neural equalizer of Nair and Moon (1997), referred to as maximum signal-to-distortion ratio (MSDR) equalizer. MSDR equalizer uses a specially designed neural architecture where all the parameters are determined theoretically. Simulation results indicate that MRAN equalizer has better performance than that of MSDR equalizer in terms of higher signal-to-distortion ratios.     

基于再生核Hilbert空间的非线性信道均衡算法

在高速无线通信领域,为消除码间干扰（ISI）必须研究非线性信道均衡技术。基于再生核希尔伯特空间（RKHS）研究非线性信道的自适应均衡算法。首先基于非线性维纳模型提出均衡器的结构,基于RKHS引入核方法,与仿射投影算法（APA）相结合推导出核仿射投影算法（KAPA）,再通过引入松弛因子得到改进的KAPA算法。用蒙特卡罗法对提出的自适应算法进行仿真,从收敛性能、误码率（BER）、跟踪能力、计算复杂度等方面与其他算法做比较。在不增加计算复杂度的情况下,极大降低了误码率,非常适合时变非线性信道均衡的应用。     

Complex-bilinear recurrent neural network for equalization of adigital satellite channel

Equalization of satellite communication using complex-bilinear recurrent neural network (C-BLRNN) is proposed. Since the BLRNN is based on the bilinear polynomial, it can be used in modeling highly nonlinear systems with time-series characteristics more effectively than multilayer perceptron type neural networks (MLPNN). The BLRNN is first expanded to its complex value version (C-BLRNN) for dealing with the complex input values in the paper. C-BLRNN is then applied to equalization of a digital satellite communication channel for M-PSK and QAM, which has severe nonlinearity with memory due to traveling wave tube amplifier (TWTA). The proposed C-BLRNN equalizer for a channel model is compared with the currently used Volterra filter equalizer or decision feedback equalizer (DFE), and conventional complex-MLPNN equalizer. The results show that the proposed C-BLRNN equalizer gives very favorable results in both the MSE and BER criteria over Volterra filter equalizer, DFE, and complex-MLPNN equalizer.     

一种单载波宽带信号非线性均衡技术

针对单载波宽带信号均衡难以收敛的问题,研究了一种基于子带分解与重构的宽带非线性均衡技术。综合利用复数子带滤波器组与判决反馈均衡器理论,给出了两种具有模式切换功能的宽带非线性均衡结构,基于最小均方算法推导了它们的均衡权值迭代更新公式,分析比较了两种结构在不同均衡模式下的收敛特点。仿真证明基于子带技术的宽带非线性均衡能适用于群时延较严重的信道,且比传统全频带均衡具有更好的收敛效果和更低的计算复杂度。     

A clustering technique for digital communications channelequalization using radial basis function networks

The application of a radial basis function network to digital communications channel equalization is examined. It is shown that the radial basis function network has an identical structure to the optimal Bayesian symbol-decision equalizer solution and, therefore, can be employed to implement the Bayesian equalizer. The training of a radial basis function network to realize the Bayesian equalization solution can be achieved efficiently using a simple and robust supervised clustering algorithm. During data transmission a decision-directed version of the clustering algorithm enables the radial basis function network to track a slowly time-varying environment. Moreover, the clustering scheme provides an automatic compensation for nonlinear channel and equipment distortion. Computer simulations are included to illustrate the analytical results.     

Pipelined functional link artificial recurrent neural network with the decision feedback structure for nonlinear channel equalization

This paper presents a computationally efficient nonlinear adaptive filter by a pipelined functional link artificial decision feedback recurrent neural network (PFLADFRNN) for the design of a nonlinear channel equalizer. It aims to reduce computational burden and improve nonlinear processing capabilities of the functional link artificial recurrent neural network (FLANN). The proposed equalizer consists of several simple small-scale functional link artificial decision feedback recurrent neural network (FLADFRNN) modules with less computational complexity. Since it is a module nesting architecture comprising a number of modules that are interconnected in a chained form, its performance can be further improved. Moreover, the equalizer with a decision feedback recurrent structure overcomes the unstableness thanks to its nature of infinite impulse response structure. Finally, the performance of the PFLADFRNN modules is evaluated by a modified real-time recurrent learning algorithm via extensive simulations for different linear and nonlinear channel models in digital communication systems. The comparisons of multilayer perceptron, FLANN and reduced decision feedback FLANN equalizers have clearly indicated the convergence rate, bit error rate, steady-state error and computational complexity, respectively, for nonlinear channel equalization.