实方阵的Moore-Penrose广义逆的MCG算法探究

证明了矩阵Moore-Penrose逆的唯一性以及建立了求矩阵Moore-Penrose逆的算法。首先将求矩阵的Moore-Penrose逆转为求解含有三个矩阵变量的矩阵方程组，其次建立求该矩阵方程组的修正共轭梯度算法(MCG算法)，给出了MCG算法的性质和收敛性证明，对于任意给定的初始矩阵该算法能在有限步迭代计算后得到矩阵的Moore-Penrose逆。最后给出数值算例，证明MCG算法在求解矩阵Moore-Penrose逆中具有很高的计算效率。     

基于CSSE-OSELM算法的软测量建模及其工业应用

针对软测量建模样本的特性,提出一种基于布谷鸟选择性集成学习的在线贯序极限学习机(CSSE-OSELML)软测量建模方法。首先,以多个OSELM组合成集成学习的框架,并给每个OSELM赋予权重并设定阈值,借助于布谷鸟算法(CS)从中选择出满足阈值条件的OSELM个体,重新组合成集成学习的子集。最终以该子集建立软测量模型,进行集成学习并做加权处理。以UCI标准数据集进行测试,同时对加氢裂化反应分馏塔航煤干点进行验证,仿真结果表明,该算法优于传统的方法,具有更高的预测精度和稳定性能。     

多点激励功率谱再现振动试验控制研究

通过对多点激励功率谱再现振动试验控制算法研究.设计基于偏相干分析理论的振动试验系统频响矩阵辨识策略,针对系统频响矩阵存在奇异点及系统频响矩阵为长方矩阵情形,设计基于求解频响矩阵广义逆和矩阵最小范数最小二乘解的Moore-Penrose逆系统解耦算法。针对传统差分修正驱动谱控制算法中存在系统功率谱自谱为负数或零值问题,通过引进比例均方根反馈修正算法,设计改进的功率谱均衡控制策略,有效避免功率谱均衡过程中自谱产生负值或零值问题。多点激励功率谱再现振动试验表明,改进的功率谱均衡控制策略对多点激励系统具有可靠、高精度的控制效果。     

友循环矩阵求逆和广义逆的Euclid算法

利用多项式的Euclid算法给出了任意域上非奇异的友循环矩阵求逆矩阵的一个新算法,该算法同时推广到用于求任意域上奇异友循环矩阵的群逆和Moore-Penrose逆,最后给出了应用该算法的数值例子。     

基于差分进化的中文情感分类集成算法研究

情感分类是一种从文本中提取情感倾向的文本分类任务。集成学习通过结合几个分类器,在情感分类任务上能够获得比个体分类器更好的分类效果。但是,由于个体分类器在数据集上的表现不同,个体分类器在集成方法中的权重难以确定。针对集成学习中个体分类器的权重优化问题,提出一种基于差分进化优化个体分类器权重的集成分类方法,并将其应用于中文情感分类。以分类准确率为适应度值,通过差分进化算法优化5种个体分类器的权重组合,在3个领域的评论语料集上进行实验。实验结果表明,与一般的集成方法相比,该方法在中文情感分类上有更好的分类效果。     

基于动态集成的遥感图像分类

鉴于多分类器集成能够获得比单个分类器更好的性能,但是对于支持向量机(support vector ma-chine,SVM),一般的集成方法很难达到效果.特提出了基于局部精度(local accuracy,LA)的动态集成算法.首先,通过多种方法产生个体分类器;其次,利用验证数据集来定义LA,LA用来衡量各个体分类器的权重以及判断是否挑选该个体分类器的标准;最后,在某研究区的遥感图像数据集上进行实验.实验结果表明,动态集成的效果要优于静态集成,特别是异类动态集成效果最好.静态集成只考虑了分类器在训练样本中的表现而没有考虑测试样本的特征,对于动态集成,可以根据测试样本在验证集上的表现来选择个体分类器,因此它展现出更好的性能.     

面向基因数据分类的旋转森林算法研究

针对基因表达数据高维和小样本的特点,介绍一种基于主成分分析的决策树集成分类算法——旋转森林.首先通过对数据属性集的随机分割,再对子集进行主成分分析变换,保留全部的主成分系数,重新组成一个稀疏矩阵.然后对变换后的数据利用非剪枝决策树集成算法进行分类.再结合ReliefF算法,选用3组基因表达数据验证算法,对比Bagging决策树和随机森林两种集成方法.结果表明旋转森林算法对基因数据具有更好的分类精度,同时验证旋转森林在较低的集成数的情况下,可以取得良好的效果.     

求鳞状因子循环矩阵的逆阵及广义逆阵的快速算法

利用多项式快速算法，给出了求鳞状因子循环矩阵的逆阵、自反g-逆、群逆及Moore-Penrose逆的快速算法。该算法避免了一般快速算法中，要计算大量的三角函数等可能带来误差及影响效率的问题。该算法仅用到鳞状因子循环矩阵的第一行元素及对角阵D中的常数d1，d2，…，dn进行计算，在计算机上实现时只有舍入误差。特别地，在有理数域上用计算机求得的结果是精确的。     

反自反矩阵的二次特征值反问题及其最佳逼近

二次特征值反问题是二次特征值问题的一个逆过程,在结构动力模型修正领域中应用非常广泛．本文由给定的部分特征值和特征向量,利用矩阵分块法、奇异值分解和Moore-Penrose广义逆,分析了二次特征值反问题反自反解的存在性,得出了解的一般表达式．然后讨论了任意给定矩阵在解集中最佳逼近解的存在性和唯一性．最后给出解的表达式和数值算法,由算例验证了结果的正确性．     

一种新的神经网络预失真器及其算法

针对高功率放大器(High Power Amplifier,HPA)的神经网络(Neural Network,NN)预失真器非直接学习方法中存在的预失真性能缺陷和直接学习方法中存在的计算复杂的弊端,本文基于非直接方法得到HPA后逆滤波器的精确辨识,利用非线件算子的运算性质及一种近似方法分别推导出了新的NN预失真器学习结构及其相应的自适应算法.该算法由HPA的后逆滤波器辅助,直接产生HPA的前逆滤波器的输出.与直接学习方法相比,它大大简化了计算复杂度.仿真结果表明,本文提出的NN预失真器学习方法可以有效地改善非直接学习方法的顶失真效果,进一步降低邻信道功率比约5dB.     

An efficient gait recognition based on a selective neural network ensemble

The neural network ensemble is a learning paradigm where a collection of neural networks is trained for the same task. Generally, the ensemble shows better generalization performance than a single neural network. In this article, a selective neural network ensemble is applied to gait recognition. The proposed method selects some neural network based on the minimization of generalization error. Since the selection rule is directly incorporated into the cost function, we can obtain adequate component networks to constitute an ensemble. Experiments are performed with the NLPR database to show the performance of the proposed algorithm. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 18, 237–241, 2008; Published online in Wiley InterScience (www.interscience.wiley.com).     

基于集成学习的强鲁棒性三维点云数据分类研究

目的通过三维扫描仪得到的点云数据往往存在很多异常值,例如噪点、遗失点和外部点等。在这些异常值存在的情况下,为了提高三维点云数据的分类精度,提出一种基于集成学习的强鲁棒性三维点云数据分类方法。方法提出一种基于最大投票法的集成学习思想,将2个深度神经网络的分类结果进行集成,从而提高网络的泛化性和准确性;采用全局特征增强和中心损失函数来优化神经网络结构,提高分类精度并增强鲁棒性。结果文中方法缩短模型训练时间至30个迭代次数,且在有噪点、丢失点和外部点的情况下分类精度均得到有效提升。结论提出的EL-3D算法在含有噪点、丢失点和外部点的情况下,鲁棒性效果要优于目前的点云分类方法。     

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance. Antenna size affects the quality factor and the radiation loss of the antenna. Metamaterial antennas can overcome the limitation of bandwidth for small antennas. Machine learning (ML) model is recently applied to predict antenna parameters. ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna. The accuracy of the prediction depends mainly on the selected model. Ensemble models combine two or more base models to produce a better-enhanced model. In this paper, a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial Antenna. Two base models are used namely: Multilayer Perceptron (MLP) and Support Vector Machines (SVM). To calculate the weights for each model, an optimization algorithm is used to find the optimal weights of the ensemble. Dynamic Group-Based Cooperative Optimizer (DGCO) is employed to search for optimal weight for the base models. The proposed model is compared with three based models and the average ensemble model. The results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.     

An ensemble model considering health index based classification for remaining useful life prediction

Accurate prediction of remaining useful life (RUL) plays an important role in the formulation of maintenance strategies. However, due to the diversity of the failure mode of equipment, there are significant differences between the degradation data, which greatly affects the accuracy of RUL prediction. In this case, an ensemble prediction model considering health index-based (HI-based) classification is proposed in this paper. Firstly, the stacked autoencoder (SAE) is employed to construct the HI. Then, the time window is used to sequentially process the HI sequence, so that many data segments with the same length can be achieved. To differentiate the data with the similar degradation process, K-means and Xgboost are selected to construct offline and online data classification models respectively. Finally, according to the results of the data classification, the ensemble model that integrates multiple machine learning methods are separately trained and then adaptively used for RUL prediction. In addition, integrating multiple methods helps to improve the generalization ability of the model. The NASA C-MAPSS dataset is applied to verify the effectiveness of the proposed method, and the results show that the proposed method achieves a higher prediction accuracy and shorter computational time than other existing prediction models.     

Hybrid Sine Cosine and Stochastic Fractal Search for Hemoglobin Estimation

The sample's hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing it. Hemoglobin (HGB) is a critical component of the human body because it transports oxygen from the lungs to the body's tissues and returns carbon dioxide from the tissues to the lungs. Calculating the HGB level is a critical step in any blood analysis job. The HGB levels often indicate whether a person is anemic or polycythemia vera. Constructing ensemble models by combining two or more base machine learning (ML) models can help create a more improved model. The purpose of this work is to present a weighted average ensemble model for predicting hemoglobin levels. An optimization method is utilized to get the ensemble's optimum weights. The optimum weight for this work is determined using a sine cosine algorithm based on stochastic fractal search (SCSFS). The proposed SCSFS ensemble is compared to Decision Tree, Multilayer perceptron (MLP), Support Vector Regression (SVR) and Random Forest Regressors as model-based approaches and the average ensemble model. The SCSFS results indicate that the proposed model outperforms existing models and provides an almost accurate hemoglobin estimate.     

基于Bagging算法构造强分类器的one class SVM导线舞动预测应用

考虑到传统物理分析方法无法解决导线舞动的预测问题,综合运用机器学习算法,对已有的舞动历史数据进行筛选和预处理,并挖掘有效信息,利用one class SVM算法解决舞动数据中负样本缺失问题,采用集成学习算法中Bagging算法建立分类器学习方法,实现了数据的随机抽样,分成不同组数据集进行相互独立的训练,避免对舞动数据过拟合,提升机器学习算法的抗噪声能力以及泛化能力,采用k折交叉验证算法进行模型的验证,并利用F1-score描述导线舞动预警模型的性能,验证了该方法在舞动预测方面的有效性。     

Ensemble learning for predicting degradation under time-varying environment

Product lifetime prediction is challenging when the product is subject to a time-varying operational environment. Most of the existing studies use some functions to explicitly specify the relationship between degradation parameters and environmental conditions so as to reveal how the degradation process evolves over time. However, in many applications, the assumptions needed for establishing these functions cannot be validated in engineering practice or they cannot accurately model the entire underlying degradation mechanism. In contrast to previous work, the focus of our study is placed on product degradation prognosis by implementing an ensemble learning method. This method combines the stochastic process modeling approach and the machine learning approach, taking advantage of these approaches to gain a more accurate and stable degradation prediction. The proposed method is demonstrated by some simulation examples and by a case study of lithium-ion battery accelerated degradation test. Both the simulation study and the real case verify the superiority of the proposed method. The case study indicates that the ensemble learning method can further help to effectively manage the energy storage and energy distribution of battery packs.     

Ensemble Learning Based on GBDT and CNN for Adoptability Prediction

By efficiently and accurately predicting the adoptability of pets, shelters and rescuers can be positively guided on improving attraction of pet profiles, reducing animal suffering and euthanization. Previous prediction methods usually only used a single type of content for training. However, many pets contain not only textual content, but also images. To make full use of textual and visual information, this paper proposed a novel method to process pets that contain multimodal information. We employed several CNN (Convolutional Neural Network) based models and other methods to extract features from images and texts to obtain the initial multimodal representation, then reduce the dimensions and fuse them. Finally, we trained the fused features with two GBDT (Gradient Boosting Decision Tree) based models and a Neural Network (NN) and compare the performance of them and their ensemble. The evaluation result demonstrates that the proposed ensemble learning can improve the accuracy of prediction.     

Development of machine learning‐based real time scheduling systems: using ensemble based on wrapper feature selection approach

There are two items that significantly enhance the generalisation ability (i.e. classification accuracy) of machine learning‐based classifiers: feature selection (including parameter optimisation) and an ensemble of the classifiers. Accordingly, the objective in this study is to develop an ensemble of classifiers based on a genetic algorithm (GA) wrapper feature selection approach for real time scheduling (RTS). The proposed approach can better enhance the generalisation ability of the RTS knowledge base (i.e. classifier) in comparison with three classical machine learning‐based classifier RTS systems, including the GA‐based wrapper feature selection mechanism, in terms of the prediction accuracy of 10‐fold cross validation as measured according to all the performance criteria. The proposed ensemble classifier RTS also provides better system performance than the three machine learning‐based RTS systems, including the GA‐based wrapper feature selection mechanism and heuristic dispatching rules, under all the performance criteria, over a long period in a flexible manufacturing system (FMS) case study.