基于计算智能方法的动态系统故障诊断技术

简要地综述了基于计算智能方法的动态系统故障诊断技术的最新进展。将计算智能方法与基于模型的方法结合，用于不确定非线性动态系统的故障诊断是这一领域新的发展趋势。重点分析了用于非线性系统故障诊断的基于状态／参数估计的计算智能方法，主要包括神经网络、模糊逻辑、遗传算法。探讨了提高诊断算法鲁棒性的途径。同时也对无模型的基于计算智能的故障诊断方法中的一些研究热点问题进行了分析。最后探讨了该领域今后的发展方向。     

Computational Intelligence Techniques for Short-Term Electric Load Forecasting

Electric load forecasting has received an increasing attention over the years by academic and industrial researchers and practitioners due to its major role for the effective and economic operation of power utilities. The aim of this paper is to provide a collective unified survey study on the application of computational intelligence (CI) model-free techniques to the short-term load forecasting of electric power plants. All four classes of CI methodologies, namely neural networks (NNs), fuzzy logic (FL), genetic algorithms (GAs) and chaos are addressed. The paper starts with some background material on model-based and knowledge-based forecasting methodologies revealing a number of key issues. Then, the pure NN-based and FL-based forecasting methodologies are presented in some detail. Next, the hybrid neurofuzzy forecasting methodology (ANFIS, GARIC and Fuzzy ART variations), and three other hybrid CI methodologies (KB-NN, Chaos-FL, Neurofuzzy-GA) are reviewed. The paper ends with eight representative case studies, which show the relative merits and performance that can be achieved by the various forecasting methodologies under a large repertory of geographic, weather and other peculiar conditions. An overall evaluation of the state-of-art of the field is provided in the conclusions.     

舰船辐射噪声功率谱特征提取及系统实现

舰船辐射噪声特征提取是水下被动声探测装置进行目标识别、分类的关键技术;针对于此,研究了基于功率谱的连续谱、线谱特征实时提取技术和工程实现;该技术以分段拟合为基础,首先提取功率谱中的连续谱特征,再从原始功率谱中减去连续谱得到线谱分布,然后进行线谱特征提取;在此摹础上,详细介绍了舰船噪声功率谱特征提取的软、硬件系统设计和实现技术;系统经过实验验证,能有效提取不同类型目标的特征,系统性能良好,可满足新一代水下智能被动探测的要求.     

用于在线签名认证的特征提取和个性化特征选择方法

提出一种在线签名认证中的特征提取和特征选择的方法.采用一种F-Tablet手写板采集签名数据.该手写板的特点是不仅可记录签名时的字形信息(x,y)序列,还可记录签名时的五维力信息(F_x,F_y,F_z,M_x,M_y)序列.从每个签名中提取3个等级共188个特征,接着定义特征重要性函数F,然后根据特征的重要性函数F的值对选取的188个特征进行排序,对F设不同的阈值就可完成不同的特征选择.在认证过程中使用SVM算法对选取的特征进行训练,然后用训练所得的模型进行验证.该方法的错误拒绝率为1.2%,错误接受率为3.7%.     

基于粗糙集和Bi-LSTM-Attention网络的电力系统暂态评估

为了降低不稳定因素对现代复杂的电力系统暂态评估的影响,在基于模糊领域单参数粗糙集和注意力机制的双向长短期记忆(Rs-Bi-LSTM-Attention)网络下,提出了一种电力系统暂态稳定评估模型.该模型首先用模糊领域单参数粗糙集对电力系统潮流数据进行属性约简,该方法既保证了准确率又提高了核心因素的权重;然后,通过Bi-...     

基于RKNd算法的暂态稳定性快速数值计算方法

RKNd方法是一类新的数值积分方法。在相同级数条件下,RKNd方法可达到的最高代数阶比传统的Runge—Kutta方法以及Runge—Kutt—Nystrom方法均高,而且具有更高的计算效率。将RKNd方法引入电力系统暂态稳定性数值计算。以IEEEl45节点电力系统为例,通过数值实验将新方法与电力系统分析中常用的传统数值计算方法进行了对比分析。数值实验结果表明,RKNd方法在计算精度和计算效率等方面均具有明显的优势,N而更适合于电力系统暂态稳定性及相似问题的数值计算。     

Computational Methods for Verification of Stochastic Hybrid Systems

Stochastic hybrid system (SHS) models can be used to analyze and design complex embedded systems that operate in the presence of uncertainty and variability. Verification of reachability properties for such systems is a critical problem. Developing sound computational methods for verification is challenging because of the interaction between the discrete and the continuous stochastic dynamics. In this paper, we propose a probabilistic method for verification of SHSs based on discrete approximations focusing on reachability and safety problems. We show that reachability and safety can be characterized as a viscosity solution of a system of coupled Hamilton-Jacobi-Bellman equations. We present a numerical algorithm for computing the solution based on discrete approximations that are derived using finite-difference methods. An advantage of the method is that the solution converges to the one for the original system as the discretization becomes finer. We also prove that the algorithm is polynomial in the number of states of the discrete approximation. Finally, we illustrate the approach with two benchmarks: a navigation and a room heater example, which have been proposed for hybrid system verification.     

Feature Selection for Reduction of Tabular Knowledge-Based Systems

Tabular knowledge-based systems are known to be extremely versatile for verification and validation of knowledge bases. However, a major disadvantage of these systems is the combinatorial explosion that accompanies addition of new attributes or condition entries in the table. One of the means of alleviating this problem in tabular knowledge-based systems is through modularization, which is the process of breaking a big comprehensive table into smaller tables that are easy to deal with. In this study, we propose and illustrate another means to deal with this problem through use of feature selection methodology. The proposed method can be used synergistically with modularization to alleviate problems associated with combinatorial explosion in tabular knowledge bases.     

神经网络分类器的特征提取和优选

结合木质胶合体缺疵质检过程,论述了用类内方差、类间方差,特征相关度三个目标函数综合测度图像特征差异的技术方法,从而实现了对神经网络输入层神经元的优选,降低了网络规模,提高了分类性能.     

区间序信息系统的无监督特征选择*

目前已有很多针对单值信息系统的无监督特征选择方法,但针对区间值信息系统的无监督特征选择方法却很少.针对区间序信息系统,文中提出模糊优势关系,并基于此关系扩展模糊排序信息熵和模糊排序互信息,用于评价特征的重要性.再结合一种综合考虑信息量和冗余度的无监督最大信息最小冗余(UmIMR)准则,构造无监督特征选择方法.最后通过实验证明文中方法的有效性.     

特征选择方法与算法的研究

特征选择的主要思想是通过去除一些包含少量或不相关的信息的特征去选择特征子集。特征选择方法可分为三大类：一是过滤式,二是封装式,三是嵌入式。鉴于目前存在大量的特征选择算法,为了能够适当地决定在特定的情况下使用哪种算法,需要提出可以依赖或判定的标准。文中的主要工作就是综述一些基本特征选择算法,根据文献中已有的理论和实验结果对特征选择方法和算法进行比较分类,然后提出一种可以依赖或判定的标准。     

PHOG特征与聚类特征选择的笑脸识别方法

基于Gabor特征的人脸表情识别系统虽具有良好的识别性能,但特征维数大、分类器复杂度高。因此,文中提出一种基于PHOG特征与聚类线性鉴别分析(CLDA)的笑脸识别方法。PHOG特征的引入在于简化系统的运算复杂度,而CLDA克服传统线性鉴别分析方法的多模态问题。实验结果表明PHOG特征免去Gabor特征在Adaboost耗时的特征选择过程,具有和Gabor特征相当或更优的识别性能,且CLDA在维数降低时,系统的识别率能得到更好保持。     

基于RgPCA的表情特征提取与选择

将RgPCA算法应用于表情图像的特征提取。其核心思想：先用PCA处理表情图像,提取表情特征;然后,将此表情特征集作为一个粗糙集用FAI算法进行约筒,从而实现表情特征选择。RgPCA算法继承了PCA在特征提取时的简洁,并充分考虑模式分类的要求．提高了特征选择的科学性。     

An Automated Feature Selection Method for Visual Inspection Systems

Automated visual inspection (AVI) systems these days are considered essential in the assembly of surface-mounted devices (SMDs) in the electronics industry. This industry has faced the problem of rapid introduction and retirement of SMD-based products with the consequent obsolescence of the inspection systems already in the assembly lines. The constant introduction of new products has caused AVI systems to become rapidly obsolete. The general goal of this research centers on developing self-training AVI systems for the inspection of SMD components. The premise is that these systems would be less prone to obsolescence. In this paper, the authors describe the methodology being used for automatically selecting the features to inspect new components. In particular, this paper explores the use of multivariate stepwise discriminant analysis techniques, such as Wilks' Lambda, in order to automate the feature selection process. All of these techniques are applied to a case study of the inspection of SMD components. Note to Practitioners—In this paper, we present a methodology that would allow the automation of the tedious task of exploring and selecting features to develop algorithms for automated visual inspection systems. In particular, the proposed method selects a subset of features among all of the known features. The chosen subset seeks to minimize inspection errors while keeping the algorithmic development time to a minimum. This is particularly useful for adapting pre-existing systems to inspect new components, especially when the characteristics of the new components are similar to those of components already inspected by the inspection system. We applied this methodology to a case of study of the inspection of surface-mounted devices.     

A Novel Feature Selection Methodology for Automated Inspection Systems

This paper proposes a new feature selection methodology. The methodology is based on the stepwise variable selection procedure, but, instead of using the traditional discriminant metrics such as Wilks' Lambda, it uses an estimation of the misclassification error as the figure of merit to evaluate the introduction of new features. The expected misclassification error rate (MER) is obtained by using the densities of a constructed function of random variables, which is the stochastic representation of the conditional distribution of the quadratic discriminant function estimate. The application of the proposed methodology results in significant savings of computational time in the estimation of classification error over the traditional simulation and cross-validation methods. One of the main advantages of the proposed method is that it provides a direct estimation of the expected misclassification error at the time of feature selection, which provides an immediate assessment of the benefits of introducing an additional feature into an inspection/classification algorithm.     

基于RgPCA的表情特征提取与选择

将RgPCA算法应用于表情图像的特征提取。其核心思想:先用PCA处理表情图像,提取表情特征;然后,将此表情特征集作为一个粗糙集用FAI算法进行约简,从而实现表情特征选择。RgPCA算法继承了PCA在特征提取时的简洁,并充分考虑模式分类的要求,提高了特征选择的科学性。     

文本分类的特征提取方法比较与改进

文本的特征提取是文本分类过程中的一个重要环节,它的好坏将直接影响文本分类的准确率。该文介绍了词条的χ2统计方法（CHI）、词条与类别的互信息（MI）、信息增益（IG）、词条的期望交叉熵（CE）等文本特征提取方法,并对其取词策略进行了改进。为了对这些特征提取方法进行系统地比较,选择了三种代表性的分类器对《读卖新闻》文本数据库进行了分类实验。实验结果表明χ2统计方法具有最好的准确率,各种改进的特征提取方法都能提高文本分类的准确率。