A clustering algorithm based TS fuzzy model for tracking dynamical system data

This paper presents an optimal fuzzy partition based Takagi Sugeno Fuzzy Model (TSFM) in which a novel clustering algorithm, known as Modified Fuzzy C-Regression Model (MFCRM), has been proposed. The objective function of MFCRM algorithm has been developed by considering of geometrical structure of input data and linear functional relation between input–output data. The MFCRM partitions the data space to create fuzzy subspaces (rules). A new validation criterion has been developed for detecting the right number of rules (subspaces) in a given data set. The obtained fuzzy partition is used to build the fuzzy structure and identify the premise parameters. Once, right number of rules and premise parameters have been identified, then consequent parameters have been identified by orthogonal least square (OLS) approach. The cluster validation index has been tested on synthetic data set. The effectiveness of MFCRM based TSFM has been validated on benchmark examples, such as Boiler Turbine system, Mackey–Glass time series data and Box–Jenkins model. The model performance is also validated through high-dimensional data such as Auto-MPG data and Boston Housing data.     

基于模糊聚类及灰色关联的软件需求分析方法

通过模糊聚类方法可以对软件需求进行类别划分;通过灰色关联方法,可以对每个类别内的需求进行重要程度排序.这种分析方法可以为需求管理提供一个科学的解决途径.     

Fault detection and diagnosis for stochastic systems via output PDFs

This paper investigates a new type of fault detection and diagnosis (FDD) problem for non-Gaussian stochastic distribution systems via the output probability density function (PDF). The PDF can be approximated by using square root B-spline expansions. In this framework, an optimal fault detection algorithm is presented by introducing the tuning parameter such that the residual is as sensitive as possible to the fault. When the fault occurs, an adaptive network parameter-updating law is designed to approximate the fault. At last, paper-making process example is given to demonstrate the efficiency of the proposed approach.     

Fault detection strategy combining NARMAX model and Bhattacharyya distance for process monitoring

The complexity of modern chemical and petrochemical plants is becoming increasingly problematic in the recent years. At the same time, the demands to ensure safety and reliability of process operations rise. Early detection of abnormal event in complex real systems decrease maintenance cost and lead to guarantee the safety of human operators and environment. In the present work, a fault detection (FD) method which exploits the advantages of black-box modeling and statistical measure for fault detection in real chemical process as a distillation column is proposed. This technique is developed by applying the Nonlinear Auto-Regressive Moving Average with eXogenous input (NARMAX) model and Bhattacharyya distance (BD). In order to determine the NARMAX model, a real data set recorded during normal operations is used. Then, the BD is used to quantify on-line the dissimilarity between the current and reference probability distributions of the residual obtained from the NARMAX model for fault detection purposes. The ability of the proposed FD approach is demonstrated using real fault of separation unit. The obtained results indicate that the developed technique produces favorable performance compared to the conventional Cumulative Sum (CUSUM) test.     

Fault detection for discrete-time fuzzy systems with measurement errors using interval observers

This paper presents an interval observer-based fault detection (FD) strategy for discrete-time T–S fuzzy systems with measurement errors. The system and measurement outputs are selected as the premise variables of plant and observer respectively. The bounds of mismatch items caused by the measurement errors are established by covering matched region, mismatched left adjacent region and right adjacent region. Piecewise Lyapunov function, taking full account of possible transitions, is employed to drive observer design condition. FD is turned into optimization problem with disturbance attenuation, fault sensitivity and nonnegativity constraints. The decision is implemented by judging whether zero is excluded from the residual interval. Finally, simulation is explored to verify the scheme.     

Fault diagnosis for analog circuits based on chaotic signal excitation

A fault diagnosis method for analog circuit is proposed in this paper. An all-purpose amplifier is taken as the typical circuit under test (CUT). The chaotic signal, which is generated by an improved Chua's circuit, is employed as the excitation signal of CUT. The algorithm for phase-space reconstruction of chaotic time series is a combination algorithm of multiple autocorrelation and Γ-test. The circuit state is estimated based on detecting the geometric change of Chua's attractor with a data-mining approach. For the purpose of information fusion, another eight features are extracted from the testing data to comprehensively determine the CUT states. A discussion is made for comparing the effectiveness of each feature according to the testing results.     

基于遗传算法的模糊证券组合投资模型

在不允许卖空的情况下,为了体现投资者对期望收益的满意度,以最小风险对应的最大方差系数为目标,期望临界收益率为约束,运用模糊优化的方法建立了证券组合投资模型,并采用遗传算法给出了模型的求解。     

基于前景检测的视差优化算法

目前立体匹配算法分两类,传统的匹配算法通过计算两幅图的像素点相似程度,采用的是一种局部优先的方法。而当前的策略主要将问题转化为求解能量方程,进而对全局空间进行优化,提高匹配精度,获得更好的视差图。但是在实际应用过程中,由于光学失真和噪声,平滑表面的镜面反射,投影缩减,透视失真,低纹理和重复纹理等影响,导致误匹配或者找不到匹配点,从而得不到有效视差。然而在特定的场合,可以利用有限的有效视差,基于前景检测以及最小二乘法,优化得到较为完整的前景视差图。     

基于模糊语义算法的科研项目立项评估模型

基于模糊理论,提出以百分比填答的模糊语义表,采用模糊语义算法,构建科研项目立项评估模型。构建过程先由回收的电子打分表,取模糊值大于模糊中位数的有效值,并提取出评估科研项目的主指标、次指标,建立科研项目评估表,供评审委员会主任、副主任、专家总体组长进行评估。由评审专家按照科研项目评估表给予各准则的权重,并根据此准则进行科研项目评分,采用模糊集方式表达,将各抽象的准则转为量化的三角模糊数,对模糊数进行排序评估。最后进行案例评估分析,对构建的模型进行验证,结果表明具有良好的优越性,为科学、客观、公正的评估和优选科研项目立项提供了一种更加可行的方法。     

基于k-means聚类算法的专利地图制作方法研究

利用专利文献,制作专利地图是有效监测和了解技术发展现状和趋势的重要手段之一,因此最近几年以来,关于专利地图的研究在知识产权领域引起了广泛的关注。本文分析了专利地图在国内外的研究现状,归纳了专利地图的功能,剖析了当前专利地图制作方法的不足,利用了专利文献中的结构化项目和非结构化项目,通过k-means聚类算法,形成语义网络,并最终制作完成可视化专利地图。而根据该专利地图,可以清晰而直观的看出所研究目标技术领域的技术发展路径。     

Fault diagnosis for processes with feedback control loops by shifted output sampling approach

Data-driven fault diagnosis of closed loop processes has been a challenge in the process control community. The issue of the interaction between the process model and the controller model exists in models directly identified from closed loop data, because for all the measured process outputs, no matter whether they are normal or faulty, they are fed back into the controllers so that the reconstruction-based contribution (RBC) as the fault diagnosis method has a severe fault smearing effect. This article proposes a novel sampling scheme which can significantly eliminate the adverse effect of modeling issues in feedback control. The identifiability condition of model parameters is satisfied in the new sampling framework so that the RBC recovers its efficiency even though the process runs under feedback control. Two benchmarks, a continuous stirred-tank heater process and the Tennessee Eastman challenge problem, are used to test the efficiency of the proposed method.     

成都市医药行业产业链招商策略研究

近年来,成都招商引资工作取得了很大突破,但缺乏系统的招商规划,处于见子打子的阶段,招商效率较低。本文根据产业链的理论得出医药行业产业链的一般形态,在实证研究的基础上从产业链的角度对成都医药行业现状进行分析,结合成都的自身特点和优劣势,总结出成都市医药产业招商引资策略。     

Multi-VSM based fuzzy adaptive cooperative control strategy for MVDC traction power supply system

This paper studies the control of a medium-voltage DC (MVDC) traction power supply system for rail transit (RT). In order to optimize the ability of DC voltage fluctuation and frequency regulation of rail trains, a fuzzy adaptive cooperative secondary control strategy based on improved virtual synchronous machine (VSM) is proposed. Firstly, the improved VSM control strategy is adopted in the traction substations (TSSs), which makes the RT trains have the external characteristics of synchronous generator and have certain inertia and damping support capacity, so as to improve the ability of the RT traction system to cope with traction network transient changes. Secondly, the virtual inertia and virtual damping can be dynamically adjusted by the system frequency deviating from the nominal steady-state frequency, and the dynamic fuzzy adjustment can be carried out by the fuzzy logic system to slow down the frequency fluctuation. Then, due to the different power supply distances and parameter settings between multiple TSSs, cooperative control is used to coordinate local TSS perceptions of information between adjacent TSS to achieve consistency of frequency response of multiple TSSs under different parameters. Besides, a time-varying switching topology handoff method is considered to select the optimal communication topology between adjacent base stations. Finally, simulation results verify the effectiveness of the proposed control strategy.     

故障树分析法及其在雷达故障检测中的应用

雷达是保障航空运输安全的重要设备,可靠性是它非常重要的参数。故障树分析法可以对设备可靠性进行精确地分析、计算和评估。文章首先介绍了故障树分析法的基本原理,然后对某雷达发射机建立故障树模型,运用集合论、布尔代数、概率论,对该模型进行定性、定量分析。结果表明,故障树分析法不仅能够描述系统结构的可靠性,指导改善系统结构,而且在系统故障检测中,能进行故障定位和诊断,预测未来设备发生故障的概率。     

模糊信息融合在电子电路故障诊断中的应用

本文提出了电子电路故障定位的多传感器模糊信息融合方法。通过测试电路中的被诊断元件的工作温度和工作电压两个物理量,得出模糊信息理论中两传感器对各待诊断元件的故障隶属度,再利用模糊信息融合,确定故障元件,并通过单传感器诊断结果与模糊信息融合诊断结果比较,说明多传感器融合的优越性。     

Community detection using hierarchical clustering based on edge-weighted similarity in cloud environment

Recently, social network has been paid more and more attention by people. Inaccurate community detection in social network can provide better product designs, accurate information recommendation and public services. Thus, the community detection (CD) algorithm based on network topology and user interests is proposed in this paper. This paper mainly includes two parts. In first part, the focused crawler algorithm is used to acquire the personal tags from the tags posted by other users. Then, the tags are selected from the tag set based on the TFIDF weighting scheme, the semantic extension of tags and the user semantic model. In addition, the tag vector of user interests is derived with the respective tag weight calculated by the improved PageRank algorithm. In second part, for detecting communities, an initial social network, which consists of the direct and unweighted edges and the vertexes with interest vectors, is constructed by considering the following/follower relationship. Furthermore, initial social network is converted into a new social network including the undirected and weighted edges. Then, the weights are calculated by the direction and the interest vectors in the initial social network and the similarity between edges is calculated by the edge weights. The communities are detected by the hierarchical clustering algorithm based on the edge-weighted similarity. Finally, the number of detected communities is detected by the partition density. Also, the extensively experimental study shows that the performance of the proposed user interest detection (PUID) algorithm is better than that of CF algorithm and TFIDF algorithm with respect to F-measure, Precision and Recall. Moreover, Precision of the proposed community detection (PCD) algorithm is improved, on average, up to 8.21% comparing with that of Newman algorithm and up to 41.17% comparing with that of CPM algorithm.     

基于聚类DEA的国防科技工业资源配置有效性研究

根据数据包络分析(DEA)理论和聚类分析原理,提出基于DEA的聚类分析方法。以投入产出效率为基准,避免主观因素的干扰,通过C2R模型和C2GS2模型不断地寻找有效前沿面,确定规模有效点和技术有效点,进行聚类。聚类结果准确、可靠,是一种既科学又有效的分析方法。之后,利用基于DEA聚类分析方法对国防科技工业资源配置效果进行分析与评价。     

Generic two-degree-of-freedom linear and fuzzy controllers for integral processes

This paper presents a new framework for the design of generic two-degree-of-freedom (2-DOF), linear and fuzzy, controllers dedicated to a class of integral processes specific to servo systems. The first part of the paper presents four 2-DOF linear PI controller structures that are designed using the Extended Symmetrical Optimum method to ensure the desired overshoot and settling time. The second part of the paper presents an original design method for 2-DOF Takagi-Sugeno PI-fuzzy controllers based on the stability analysis theorem. Experimental results for the speed control of a servo system with variable load illustrate the performance of the new generic control structures.     

Data-driven fault diagnosis for an automobile suspension system by using a clustering based method

This paper concerns the issues of fault diagnosis and monitoring for an automobile suspension system where only accelerator sensors in the four corners of the car body are available. A clustering based method is proposed to detect the fault happened in the spring, and the Fisher discriminant analysis is applied to isolate the root factor for the fault. Different from most of the existing approaches, the pure data-driven characteristic enables this method to serve as an on-line fault diagnosis and monitoring tool without suspension model or fault features known as a prior. Moreover, this method can classify different reductions in the spring coefficient into one fault rather than different faults. The effectiveness of the proposed method is finally illustrated on an automobile suspension benchmark.