基于稀疏和近邻保持的极限学习机降维

近邻与稀疏保持投影已被广泛应用于降维方法,通过优化得到满足近邻结构或稀疏结构的降维投影矩阵,然而这类方法多数只考虑单一结构特征.此外,多数非线性降维方法无法求出显式的映射函数,极大地限制了降维方法的应用.为克服这些问题,本文借鉴极限学习机的思想,提出面向聚类的基于稀疏和近邻保持的极限学习机降维算法（SNP-ELM）.SNP-ELM算法是一种非线性无监督降维方法,在降维过程中同时考虑数据的稀疏结构与近邻结构.在人造数据、Wine数据和6个基因表达数据上进行实验,实验结果表明该算法优于其他降维方法.     

自适应局部线性嵌入算法

局部线性嵌入算法（LLE）因其较低的计算复杂度和高效性适用于很多降维问题,新的自适应局部线性嵌入（ALLE）算法对数据进行非线性降维,提取高维数据的本质特征,并保持了数据的全局几何结构特征,对比实验结果表明了该算法对于非理想数据的降维结果均优于LLE算法。     

基于降维BP神经网络的高维数据分类研究

为确保高维数据的神经网络分类精度,提出了先降维后分类的方法。采用主成分分析（PCA）法实现高维数据的降维。通过分析传统BP算法,提出分两步来更新网络权值的扰动BP学习方法。采用MATLAB对降维分类算法的分类精度和误差收敛速度进行分析。仿真结果显示：先降维再采用扰动BP网络进行高维数据分类可大大提高数据的分类精度和训练速度。     

适合近红外光谱数据特征的降维方法对比分析

因近红外光谱具有波长点多、谱带归属困难、光谱重叠严重及光谱分布结构未知等问题,在进行近红外光谱关键特征提取和数据特征空间映射时难以准确获知合适降维方法。为了解决该问题,本文对比分析了典型线性和非线性降维方法 ,并用烟叶近红外光谱数据从数据降维可视化和分类准确性识别率角度分别进行了实验验证。结果表明,线性降维算法,特别是PCA、LDA算法,比较适合应用于烟叶近红外光谱降维分析中,非线性降维算法因其泛化学习能力与推广能力差以及本征维数估计困难不适合应用于近红外光谱降维分析。     

基于PSO-ICA的文本分类研究

文本分类在采用向量空间模型（VSM）表达文本特征时,容易出现特征向量高维且稀疏的现象,为了对原始的文本特征向量进行有效简化,提出了一种基于粒子群（PSO）优化独立分量分析（ICA）进行降维的方法,并将其运用到文本分类中。在该算法中,以负熵作为粒子群算法的适应度函数,依据其高斯性原理作为独立性判别标准对分离矩阵进行自适应更新。实验结果表明,相比于传统的特征降维方法,该方法可以解决高维度文本特征向量降维困难的问题,使得文本分类的效率、准确率显著提升。     

基于自适应最近邻的局部保持投影算法

局部保持投影（LPP）是一种新的数据降维技术,但其本身是一种非监督学习算法,对于分类问题效果不是太好。基于自适应最近邻,结合LPP算法,提出了一种有监督的局部保持投影算法（ANNLPP）。该方法通过修改LPP算法中的权值矩阵,在降维的同时,增加了类别信息,是一种有监督学习算法。通过二维数据可视化和UMIST、ORL 人脸识别实验,表明该方法对于分类问题具有较好的降维效果。     

非线性系统的参数空间分割辨识方法

本文提出了一种新的非线性系统Volterra级数模型辨识方法,为非线性系统辨识中 的“维数灾难”问题提供了一种满意的解决．算法中参数空间分割和模型辨识同时完成,降 维依据采用输出拟合结果的均方误差,最终得到输出拟合均方误差意义上的准最优解．本算 法也可以作为非线性系统模型的结构辨识算法,并可以直接推广应用于其它很大一类非线性 系统模型．仿真试验结果表明,算法计算量小,精度高,并具有较好的稳定性,可以应用于 在线实时辨识．     

全局与局部判别信息融合的转子故障数据集降维方法研究

针对传统的数据降维方法无法兼顾保持全局特征信息与局部判别信息的问题,提出一种核主元分析（Kernel principal component analysis,KPCA）和正交化局部敏感判别分析（Orthogonal locality sensitive discriminant analysis,OLSDA）相结合的转子故障数据集降维方法.该方法首先利用KPCA算法有效降低数据集的相关性、消除冗余属性,由此实现了最大程度地保留原始数据全局非线性信息的作用;然后利用OLSDA算法充分挖掘出数据的局部流形结构信息,达到了提取出具有高判别力低维本质特征的目的.上述方法的特点是通过同时进行的正交化处理可避免局部子空间结构发生失真,采用三维图直观显示出低维结果,以低维特征子集输入最近邻分类器（K-nearest neighbor,KNN）的识别率和聚类分析之类间距Sb、类内距Sw作为衡量降维效果的指标.实验表明该方法能够全面地提取出全局与局部判别信息,使故障分类更清晰,相应地识别准确率得到了明显提升.该研究可为解决高维和非线性机械故障数据集的可视化与分类问题,提供理论参考依据.     

混合编码差分进化粒子群算法及多示例学习的高光谱影像降维与分类

目的 高光谱遥感影像由于其巨大的波段数直接导致信息的高冗余和数据处理的复杂,这不仅带来庞大的计算量,而且会损害分类精度。因此,在对高光谱影像进行处理、分析之前进行降维变得非常必要。分类作为一种重要的获取信息的手段,现有的基于像素点和图斑对象特征辨识地物种类的方法在强噪声干扰训练样本条件下精度偏低,在对象的基础上,将光谱和空间特征相似的对象合并成比其还要大的集合,再按照各个集合的光谱和空间特征进行分类,则不容易受到噪声等因素的干扰。方法 提出混合编码差分进化粒子群算法的双种群搜索策略进行降维,基于支持向量机的多示例学习算法作为分类方法,构建封装型降维与分类模型。结果 采用AVIRIS影像进行实验,本文算法相比其他相近的分类方法能获得更高的分类精度,达到96.03%,比其他相近方法中最优的像元级的混合编码的分类方法精度高出0.62%。结论 在针对强干扰的训练样本条件下,本文算法在降维过程中充分发挥混合编码差分进化算法的优势,分类中训练样本中的噪声可以看做多示例学习中训练包"歧义性"的特定表现形式,有效提高了分类的精度。     

基于流形学习和SVM的Web文档分类算法

为解决Web文档分类问题,提出一种基于流形学习和SVM的Web文档分类算法。该算法利用流形学习算法LPP对训练集中的高维Web文档空间进行非线性降维,从中找出隐藏在高维观测数据中有意义的低维结构,在降维后的低维特征空间中利用乘性更新规则的优化SVM进行分类预测。实验结果表明该算法以较少的运行时间获得更高的分类准确率。     

Fault isolation for nonlinear dynamic systems based on parameter intervals

The study of fault detection and isolation for nonlinear dynamic systems has been receiving significant attention. Up to now few literatures pay attention to the speed of fault isolation. However, it is a crucial problem for the design of the fault-tolerant control (FTC) of the nonlinear dynamic systems. In this article a new method of fault isolation for nonlinear dynamic systems is proposed. The method is based on the monotonous characteristic of the prediction error of the observer with respect to singular parameter difference between the system and the observer. The proposed method has the advantage of the methods based on adaptive observers that fits a large kind of nonlinear dynamic systems, while it does not have their disadvantage that take a long time to identify the system parameter: Therefore the fault isolation of this method is quicker. The performance of the method is illustrated by simulation results using a nonlinear dynamic model of an alcoholic fermentation process.     

风力发电系统传感器故障诊断

针对非线性风力发电系统,提出了一种基于滑模观测器的传感器故障诊断方法.基于考虑传感器加性故障的非线性动态模型,利用T--S模糊理论建立风力发电系统全局T--S模型,设计模糊T--S系统滑模故障观测器,产生对故障具有敏感性的残差,实现故障检测.通过等价输出控制方法来维持滑模运动,直接获取故障信息,重构传感器故障.最后以三叶片水平轴风力发电系统为例,仿真验证了该方法的有效性与可靠性.     

双轴转子系统非线性动特性测试研究与实现

以双轴转子系统为对象,针对其故障非线性动特性的测试与系统分析进行深入研究.通过分析双轴转子系统典型故障的动特性,提取实验台旋转振动信号包含的特征信息进行故障诊断.结合使用.NET以及Win32 SDK为开发平台,设计和实现了双轴转子系统非线性动特性测试系统.结果表明,系统可实现转子实验台实验数据的采集以及非线性动特性的分形维数计算分析和图形输出等功能,为深入研究系统运行过程的分形维数变化提供了分析平台,对研究故障演化有着重要的应用价值.     

Locally Linear Back-propagation Based Contribution for Nonlinear Process Fault Diagnosis

This paper proposes a novel locally linear back-propagation based contribution (LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder (AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution (RBC), the propagation of fault information is described by using back-propagation (BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well, and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.      

基于核主元分析与神经网络的传感器故障诊断新方法

提出综合利用核函数主元分析(KPCA)和神经网络的方法实现非线性系统内传感器故障的检测和识别,克服了以往核函数主元分析法只能给出故障检测结果,却无法对故障进行识别的缺陷,并给出了在特征空间中计算残差的简单方法.最后,对一个简单的多变量过程进行了故障检测和识别,验证了这一诊断策略的有效性.     

Unknown Input Observer-based Actuator and Sensor Fault Estimation Technique for Uncertain Discrete Time Takagi-Sugeno Systems

This paper presents an Unknown Input robust Observer (UIO) capable of simultaneously estimate both sensor fault and system states. The system is assumed to be discrete-time Takagi-Sugeno (T-S) Fuzzy with uncertainties. An augmented system is obtained from the dynamic fault model and original system. Afterward, a UIO is designed for the augmented system aiming at decoupling process disturbances. Its design is obtained by using an H_∞ optimization technique and developed to maintain the observer stable, reducing the non-decoupled process disturbances effect. The proposed method is validated by two numerical examples as it is compared to a regular UIO technique and the extended Kalman filter. Results show the proposed technique presents better performance when the dynamic system is not purely nonlinear even if the same tuning parameters are chosen. Although other techniques are not able to ensure the error limitation, the proposed one is capable of it even in nonlinear systems.

     

非线性电路的神经网络故障诊断方法

针对非线性动态电子电路,提出一种基于神经网络的故障诊断方法。通过故障字典的建立,对电路故障响应进行预处理后得到的故障特征作为神经网络的输入,然后利用神经网络对各种状态下的特征向量进行分类决策,对故障类别进行辨识,并对电路进行了可测性分析,从而实现非线性电路的故障诊断。详细的仿真过程及结果表明, 该方法有效地解决了非线性电路辨识难的问题,能较好地对故障模式进行分类,取得了满意的诊断效果。     

基于输出PDF的浮选故障检测和诊断方法

针对浮选中泡沫尺寸分布的特殊性,如非高斯分布,左偏斜,高峰值等,常规分析方法无法准确描述尺寸分布的特点,因此无法准确检测和诊断浮选过程中出现的故障。提出对泡沫尺寸分布的输出概率密度函数(PDF)的统计分析,形成了一种新的浮选过程故障检测和诊断方法。通过采用自设计的核方法逼近将输出PDF转化为动态权系数,建立带有时滞的非线性不确定性权动态模型,基于线性矩阵不等式设计得到可行的故障检测和诊断算法。通过仿真验证分析,证明此算法的有效性。结合现场浮选过程,讨论了此方法的应用前景和优势。     

Isolation and handling of sensor faults in nonlinear systems

This work considers the problem of sensor fault isolation and fault-tolerant control for nonlinear systems subject to input constraints. The key idea is to design fault detection residuals and fault isolation logic by exploiting model-based sensor redundancy through a state observer. To this end, a high-gain observer is first presented, for which the convergence property is rigorously established, forming the basis of the residual design. A bank of residuals are then designed using a bank of observers, with each driven by a subset of measured outputs. A fault is isolated by checking which residuals breach their thresholds according to a logic rule. After the fault is isolated, the state estimate generated using measurements from the healthy sensors is used in closed-loop to maintain nominal operation. The implementation of the fault isolation and handling framework subject to uncertainty and measurement noise is illustrated using a chemical reactor example.     

Fault detection and diagnosis approach based on nonlinear parity equations and its application to leakages and blockages in the drying section of a board machine

This study aims at providing a fault detection and diagnosis (FDD) approach based on nonlinear parity equations identified from process data. Process knowledge is used to reduce the process nonlinearity from high to low-dimensional nonlinear functions representing common process devices, such as valves, and incorporating the monotonousness properties of the dependencies between the variables. The fault detection approach considers the obtained process model to be nonlinear parity equations, and fault diagnosis is carried out with the standard structured residual method. The applicability of the approach to complex flow networks controlled by valves is tested on the drying section of an industrial board machine, in which the key problems are leakages and blockages of valves and pipes in the steam–water network. Nonlinear model equations based on the mass balance of different parts of the network are identified and validated. Finally, fault detection and diagnosis algorithms are successfully implemented, tested, and reported.