Simplified MMSE Detectors for Turbo Receiver in BICM MIMO Systems

In this article, two methods adopting simplified minimum mean square error (MMSE) filter with soft parallel interference cancellation (SPIC) are discussed for turbo receivers in bit interleaved coded modulation (BICM) multiple-input multiple-output (MIMO) systems. The proposed methods are utilized in the non-first iterative process of turbo receiver to suppress residual interference and noise. By modeling the components of residual interference after SPIC plus the noise as uncorrelated Gaussian random variables, the matrix inverse for weighting vector of conventional MMSE becomes unnecessary. Thus the complexity can be greatly reduced with only slight performance deterioration. By introducing optimal ordering to SPIC, performance gap between simplified MMSE and conventional MMSE further narrows. Monte Carlo simulation results confirm that the proposed algorithms can achieve almost the same performance as the conventional MMSE SPIC in various MIMO configurations, but with much lower computational complexity.     

基于逐分量软干扰删除的迭代线性检测算法

提出了一种新型基于调制符号的分量进行干扰删除和线性最小均方误差滤波的软输入软输出检测算法,并采用软输入软输出的多入多出MIMO检测器和信道编码串行级联的迭代检测译码IDD结构。该算法充分利用正交调制符号同相分量和正交分量的独立衰落特性,达到检测中更加准确的软干扰删除。外信息转移EXIT图表明该算法比传统的逐符号软删除算法具有更低的临界信噪比。数值仿真也验证了提出的基于调制符号的分量删除的线性检测算法比采用调制符号级的删除具有更优的误码性能,并且仍然具有低复杂度的特性。     

基于PSO-ELM算法的红外目标模拟器校准数据拟合方法研究

提高红外目标模拟器校准数据的拟合精度,对于红外目标的辐射照度等辐射特性的测量有着重要意义;针对校准数据具有很强的非线性,传统的拟合算法精度不高的问题,引入一种基于粒子群算法优化的极限学习机算法（PSO-ELM）,以标准黑体辐射温度作为输入因子,以MCT探测器实际测量出的辐射照度作为输出因子,建立PSO-ELM模型,利用粒子群算法（PSO）对连接隐藏神经元和输入层的权值和隐藏神经元阈值进行优化,拟合出输入参数和输出参数之间的非线性关系;这两个参数的优化提高了极限学习机算法（ELM）的性能,该方法的主要优点是具有较强的容错性、较好的对复杂非线性数据处理性能和ELM算法参数设置上的优化机制;通过与GA-ELM模型、ELM模型进行对,验证了与传统数据拟合方法相比,基于PSO-ELM的方法拟合精度有了很大提高,为红外目标模拟器校准数据拟合提供了新的方法。     

改进鱼群算法优化的ELM在乳腺肿瘤辅助诊断中的应用研究

针对传统极限学习机的输入权值矩阵和隐含层偏差是随机给定进而可能会导致在乳腺肿瘤的辅助诊断应用研究中存在精度明显不足的情况,提出用改进鱼群算法优化ELM方法。在完成对乳腺肿瘤有效的辅助诊断的过程中,本研究工作充分利用ELM能快速地完成训练过程且具有很好的泛化能力的特点,并结合用改进鱼群算法对ELM的隐含层偏差进行优化,构造出了乳腺肿瘤与从乳腺肿瘤样本数据中提取的10个特征向量之间的非线性映射关系。将本文提出的乳腺肿瘤识别方法的仿真结果与AFSA-ELM方法、ELM方法、LVQ方法、BP方法的仿真结果分别从识别准确率、假阴性率、学习速度三个方面做对比分析,仿真结果表明,本文所提方法对乳腺肿瘤诊断具有较高的分类识别准确率、假阴性率以及较快的学习速率。     

基于改进极限学习机算法的行为识别

重点研究了极限学习机ELM对行为识别检测的效果。针对在线学习和行为分类上存在计算复杂性和时间消耗大的问题,提出了一种新的行为识别学习算法(ELM-Cholesky)。该算法首先引入了基于Cholesky分解求ELM的方法,接着依据在线学习期间核函数矩阵的更新特点,将分块矩阵Cholesky分解算法用于ELM的在线求解,使三角因子矩阵实现在线更新,从而得出一种新的ELM-Cholesky在线学习算法。新算法充分利用了历史训练数据,降低了计算的复杂性,提高了行为识别的准确率。最后,在基准数据库上采用该算法进行了大量实验,实验结果表明了这种在线学习算法的有效性。     

Extreme learning machine for regression and multiclass classification

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the "generalized" single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.     

基于收缩极限学习机的故障诊断鲁棒方法

为降低特征噪声对分类性能的影响,提出一种基于极限学习机(extreme learning machine,ELM)的收缩极限学习机鲁棒算法模型(CELM)。采用自编码器对输入数据进行重构,将隐层输出值关于输入的雅克比矩阵的F范数引入到目标函数中,提取出更具鲁棒性的抽象特征表示,利用提取到的新特征对常规的ELM层进行训练,提高方法的鲁棒性。对Mnist、UCI数据集、TE过程数据集以及添加不同强度的混合高斯噪声之后的Mnist数据集进行仿真,实验结果表明,提出的方法较ELM、HELM具有更高的分类精度和更好的鲁棒性。     

一种新的OFDMA系统上行同步的联合估算方法

正交频分复用多址接入（OFDMA）是以正交频分复用技术（OFDM）为基础的多用户接入技术,作为IEEE802．16e的主流技术,必将推动整个蜂窝移动通信的发展。在OFDMA系统中,同步误差对系统性能具有重要的影响,不仅仅会破坏子载波之间的正交性,造成信道间干扰（ICI）和符号间干扰（ISI）,而且还会引人多址干扰（MAI）。为了最大限度地消除同步误差对通信系统性能的影响,文中提出一种时域新型同步算法,对多用户OFDMA系统上行的时偏和频偏进行联合估计。分析与仿真结果表明,该方法同步精度优于传统的单独考虑频偏或者时偏的估计方法,且在与传统估计方法保持相同性能时还保持了较低的复杂度,有效地提高了系统性能。     

Stacked autoencoder based deep random vector functional link neural network for classification

Extreme learning machine (ELM), which can be viewed as a variant of Random Vector Functional Link (RVFL) network without the input–output direct connections, has been extensively used to create multi-layer (deep) neural networks. Such networks employ randomization based autoencoders (AE) for unsupervised feature extraction followed by an ELM classifier for final decision making. Each randomization based AE acts as an independent feature extractor and a deep network is obtained by stacking several such AEs. Inspired by the better performance of RVFL over ELM, in this paper, we propose several deep RVFL variants by utilizing the framework of stacked autoencoders. Specifically, we introduce direct connections (feature reuse) from preceding layers to the fore layers of the network as in the original RVFL network. Such connections help to regularize the randomization and also reduce the model complexity. Furthermore, we also introduce denoising criterion, recovering clean inputs from their corrupted versions, in the autoencoders to achieve better higher level representations than the ordinary autoencoders. Extensive experiments on several classification datasets show that our proposed deep networks achieve overall better and faster generalization than the other relevant state-of-the-art deep neural networks.     

Random forests-based extreme learning machine ensemble for multi-regime time series prediction

Accurate and timely predicting values of performance parameters are currently strongly needed for important complex equipment in engineering. In time series prediction, two problems are urgent to be solved. One problem is how to achieve the accuracy, stability and efficiency together, and the other is how to handle time series with multiple regimes. To solve these two problems, random forests-based extreme learning machine ensemble model and a novel multi-regime approach are proposed respectively, and these two approaches can be integrated to achieve better performance. First, the extreme learning machine (ELM) is used in the proposed model because of its efficiency. Then the regularized ELM and ensemble learning strategy are used to improve generalization performance and prediction accuracy. The bootstrap sampling technique is used to generate training sample sets for multiple base-level ELM models, and then the random forests (RF) model is used as the combiner to aggregate these ELM models to achieve more accurate and stable performance. Next, based on the specific properties of turbofan engine time series, a multi-regime approach is proposed to handle it. Regimes are first separated, then the proposed RF-based ELM ensemble model is used to learn models of all regimes, individually, and last, all the learned regime models are aggregated to predict performance parameter at the future timestamp. The proposed RF-based ELM ensemble model and multi-regime approaches are evaluated by using NN3 time series and NASA turbofan engine time series, and then the proposed model is applied to the exhaust gas temperature prediction of CFM engine. The results demonstrate that the proposed RF-based ELM ensemble model and multi-regime approach can be accurate, stable and efficient in predicting multi-regime time series, and it can be robust against overfitting.     

基于朴素贝叶斯的室内VLC网络天线选择方法

在室内多天线多用户可见光通信(VLC)网络中,为了改善在发射天线和用户数量增多的情况下,最优天线选择算法存在时间复杂度过高问题,将朴素贝叶斯(NB)方法应用于室内多用户VLC网络下行链路发光二极管(LED)选择问题中.首先,将该LED选择任务建模为多分类问题,利用用户已知信道状态信息生成训练样本集,并通过VLC网络多用户通信和速率最大生成对应类标签;其次,利用生成的训练样本集,通过NB方法得到分类器模型;最后,将训练得到的分类器模型应用于新用户的LED选择.仿真分析表明,与最优多用户VLC网络LED选择算法相比,所提出的基于NB的LED选择方案可以有效地降低时间复杂度,在算法复杂度和用户传输和速率之间实现了较好的平衡.     

Robust adaptive spread-spectrum receiver with neural netpreprocessing in non-Gaussian noise

Multiuser communications channels based on code division multiple access (CDMA) technique exhibit non-Gaussian statistics due to the presence of highly structured multiple access interference (MAI) and impulsive ambient noise. Linear adaptive interference suppression techniques are attractive for mitigating MAI under Gaussian noise. However, the Gaussian noise hypothesis has been found inadequate in many wireless channels characterized by impulsive disturbance. Linear finite impulse response (FIR) filters adapted with linear algorithms are limited by their structural formulation as a simple linear combiner with a hyperplanar decision boundary, which are extremely vulnerable to impulsive interference. This raises the issues of devising robust reception algorithms accounting at the design stage the non-Gaussian behavior of the interference. We propose a multiuser receiver that involves an adaptive nonlinear preprocessing front-end based on a multilayer perceptron neural network, which acts as a mechanism to reduce the influence of impulsive noise followed by a postprocessing stage using linear adaptive filters for MAI suppression. Theoretical arguments supported by promising simulation results suggest that the proposed receiver, which combines the relative merits of both nonlinear and linear signal processing, presents an effective approach for joint suppression of MAI and non-Gaussian ambient noise.     

Model selection of extreme learning machine based on multi-objective optimization

As a novel learning algorithm for single-hidden-layer feedforward neural networks, extreme learning machines (ELMs) have been a promising tool for regression and classification applications. However, it is not trivial for ELMs to find the proper number of hidden neurons due to the nonoptimal input weights and hidden biases. In this paper, a new model selection method of ELM based on multi-objective optimization is proposed to obtain compact networks with good generalization ability. First, a new leave-one-out (LOO) error bound of ELM is derived, and it can be calculated with negligible computational cost once the ELM training is finished. Furthermore, the hidden nodes are added to the network one-by-one, and at each step, a multi-objective optimization algorithm is used to select optimal input weights by minimizing this LOO bound and the norm of output weight simultaneously in order to avoid over-fitting. Experiments on five UCI regression data sets are conducted, demonstrating that the proposed algorithm can generally obtain better generalization performance with more compact network than the conventional gradient-based back-propagation method, original ELM and evolutionary ELM.     

Malicious Domain Name Detection Based on Extreme Machine Learning

Malicious domain detection is one of the most effective approaches applied in detecting Advanced Persistent Threat (APT), the most sophisticated and stealthy threat to modern network. Domain name analysis provides security experts with insights to identify the Command and Control (C&C) communications in APT attacks. In this paper, we propose a machine learning based methodology to detect malware domain names by using Extreme Learning Machine (ELM). ELM is a modern neural network with high accuracy and fast learning speed. We apply ELM to classify domain names based on features extracted from multiple resources. Our experiment reveals the introduced detection method is able to perform high detection rate and accuracy (of more than 95%). The fast learning speed of our ELM based approach is also demonstrated by a comparative experiment. Hence, we believe our method using ELM is both effective and efficient to identify malicious domains and therefore enhance the current detection mechanism of APT attacks.     

Synthesis of run-to-run repetitive control methods using finite impulse response models

This paper presents linear quadratic (LQ) repetitive control (RC) methods for processes represented by a conventional FIR model and a circulant FIR model. The latter, which represents a FIR system under the assumption of a cyclic steady state, is named as such as its input–output map is represented by a circulant matrix. Using the complete frequency resolving property of a circulant matrix, a special tuning method for the LQ weights is proposed. Performance of the proposed method is investigated through numerical examples.     

码移参考超宽带收发机及其在WSN环境中的性能分析

介绍了一种新型的基于码移参考技术的超宽带收发机,通过在MATLAB上建模仿真,分析比较了在无线传感器网络应用环境下码移参考超宽带系统的误比特性能。仿真结果表明,采用码移参考技术的超宽带接收机具有比传统的传输参考接收机更好的抗窄带干扰性能,并具有较低的实现复杂度,更适合于无线传感器网络的应用。此外,偏移码字的选择对系统的抗窄带干扰性能有较大影响,采用持续时间短或者随机化特性好的码字,系统的抗窄带干扰能力较强。这些结论为码字的优化设计和系统性能的改进提供了依据。     

Estimation of effluent quality using PLS-based extreme learning machines

The accurate and reliable measurement of effluent quality indices is essential for the implementation of successful control and optimization of wastewater treatment plants. In order to enhance the estimate performance in terms of accuracy and reliability, we present a partial least-squares-based extreme learning machine (called PLS-ELM) in this paper. The partial least squares (PLS) regression is applied to the ELM framework to improve the algebraic property of the hidden output matrix, which can be ill-conditional due to the high multicollinearity of the hidden layer output. The main idea behind our proposed PLS-ELM is to achieve a robust generalization performance by extracting a reduced number of latent variables from the hidden layer and using orthogonal projection operations. The results from a case study of a municipal wastewater treatment plant show that the PLS-ELM can effectively capture the input–output relationship with favorable performance against the conventional ELM.     

MIMO-MC-CDMA系统分层空时非线性预编码

针对垂直分层空时方案（VBLAST）传统检测存在误层传输效应及复杂度高的问题,提出了一种多用户MIMO-MC-CDMA下行链路系统中基于QR分解的VBLAST非线性模代数预编码算法,该算法首先采用QR分解获得预编码矩阵,然后在发射端MC-CDMA子载波信道间进行非线性模代数THP预编码,可以有效地消除分层空时码的误层传输效应。在接收端采用迫零与最小均方误差准则,降低了下行接收机的复杂度。仿真结果表明,提出的算法比传统检测算法有效改善了系统的误码性能。     

基于Teager-Kaiser算子的差分码移参考超宽带接收机

为提升差分码移参考DCSR(Differential Code Shifted Reference)超宽带接收机在窄带干扰环境中的接收性能,提出了一种基于Teager-Kaiser算子(TKO)的改进型DCSR接收机结构。通过TKO的非线性处理,可以使窄带干扰能量集中在直流附近的低频段,从而可以通过一个模拟高通滤波器将其滤除。仿真结果表明本文提出的改进结构可以有效地抑制窄带干扰,并且在不存在窄带干扰的环境中其性能仍优于传统的差分码移参考接收机和传输参考接收机。改进后的TKO-DCSR性能提升明显且实现复杂度增加不多,可以较好地满足无线传感器网络的应用需求。     

基于神经网络量子算法的多用户检测器

利用遗传量子算法和Hopfield神经网络,提出了一种融合两种算法优点的神经网络量子算法,并将其应用到CDMA通信系统的多用户检测问题中。所提算法把神经网络嵌入到遗传量子算法的每一代中,可进一步提高量子种群的适应度函数值。通过混合神经网络到GQA中,还可加快GQA的收敛速度进而减少算法的计算复杂度。另外,GQA所提供的良好初值改善了HNN的性能,嵌入的HNN也提高了GQA的性能。仿真结果证明了该方法的抗多址干扰能力和抗远近效应能力都优于传统检测器和一些应用智能算法的多用户检测器。