A New Algorithm for Black-start Zone Partitioning Based on Fuzzy Clustering Analysis

On the process of power system black start after an accident, it can help to optimize the resources allocation and accelerate the recovery process that decomposing the power system into several independent partitions for parallel recovery. On the basis of adequate consideration of fuzziness of black-start zone partitioning, a new algorithm based on fuzzy clustering analysis is presented. Characteristic indexes are extracted fully and accurately. The raw data matrix is made up of the electrical distance between every nodes and blackstart resources. Closure transfer method is utilized to get the dynamic clustering. The availability and feasibility of the proposed algorithm are verified on the New-England 39 bus system at last.

Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means

In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.

Discrete Variable Structural Optimization based on Multidirectional Fuzzy Genetic Algorithm

Round method is the common method for discrete variable optimization in optimal design of complex mechanical structures;however,it has some disadvantages such as poor precision,simple model and lacking of working conditions＇ description,etc.To solve these problems,a new model is constructed by defining parameterized fuzzy entropy,and the rationality of parameterized fuzzy entropy is verified.And a new multidirectional searching algorithm is further put forward,which takes information of actual working conditions into consideration and has a powerful local searching capability.Then this new algorithm is combined with the GA by the fuzzy clustering algorithm（FCA）.With the application of FCA,the optimal solution can be effectively filtered so as to retain the diversity and the elite of the optimal solution,and avoid the structural re-analysis phenomenon between the two algorithms.The structure design of a high pressure bypass-valve body is used as an example to make a structural optimization by the proposed HGA and finite element method（FEM）,respectively.The comparison result shows that the improved HGA fully considers the characteristic of discrete variable and information of working conditions,and is more suitable to the optimal problems with complex working conditions.Meanwhile,the research provides a new approach for discrete variable structure optimization problems.

Employment Quality EvaluationModel Based on Hybrid Intelligent Algorithm

In order to solve the defect of large error in current employment quality evaluation,an employment quality evaluation model based on grey correlation degree method and fuzzy C-means(FCM)is proposed.Firstly,it analyzes the related research work of employment quality evaluation,establishes the employment quality evaluation index system,collects the index data,and normalizes the index data;Then,the weight value of employment quality evaluation index is determined by Grey relational analysis method,and some unimportant indexes are removed;Finally,the employment quality evaluation model is established by using fuzzy cluster analysis algorithm,and compared with other employment quality evaluation models.The test results show that the employment quality evaluation accuracy of the design model exceeds 93%,the employment quality evaluation error can meet the requirements of practical application,and the employment quality evaluation effect is much better than the comparison model.The comparison test verifies the superiority of the model.

Water quality assessment for Ulansuhai Lake using fuzzy clustering and pattern recognition

Water quality assessment of lakes is important to determine functional zones of water use.Considering the fuzziness during the partitioning process for lake water quality in an arid area,a multiplex model of fuzzy clustering with pattern recognition was developed by integrating transitive closure method,ISODATA algorithm in fuzzy clustering and fuzzy pattern recognition.The model was applied to partition the Ulansuhai Lake,a typical shallow lake in arid climate zone in the west part of Inner Mongolia,China and grade the condition of water quality divisions.The results showed that the partition well matched the real conditions of the lake,and the method has been proved accurate in the application.

Application of Algorithm CARDBK in Document Clustering

In the K-means clustering algorithm, each data point is uniquely placed into one category. The clustering quality is heavily dependent on the initial cluster centroid. Different initializations can yield varied results; local adjustment cannot save the clustering result from poor local optima. If there is an anomaly in a cluster, it will seriously affect the cluster mean value. The K-means clustering algorithm is only suitable for clusters with convex shapes. We therefore propose a novel clustering algorithm CARDBK—＂centroid all rank distance（CARD）＂ which means that all centroids are sorted by distance value from one point and ＂BK＂ are the initials of ＂batch K-means＂—in which one point not only modifies a cluster centroid nearest to this point but also modifies multiple clusters centroids adjacent to this point, and the degree of influence of a point on a cluster centroid depends on the distance value between this point and the other nearer cluster centroids. Experimental results showed that our CARDBK algorithm outperformed other algorithms when tested on a number of different data sets based on the following performance indexes： entropy, purity, F1 value, Rand index and normalized mutual information（NMI）. Our algorithm manifested to be more stable, linearly scalable and faster.

Integrated parallel forecasting model based on modified fuzzy time series and SVM

A dynamic parallel forecasting model is proposed, which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is improved in outliers operation and distance in the clusters and among the clusters. Firstly, the input data sets are optimized and their coherence is ensured, the region scale algorithm is modified and non-isometric multi scale region fuzzy time series model is built. At the same time, the particle swarm optimization algorithm about the particle speed, location and inertia weight value is improved, this method is used to optimize the parameters of support vector machine, construct the combined forecast model, build the dynamic parallel forecast model, and calculate the dynamic weight values and regard the product of the weight value and forecast value to be the final forecast values. At last, the example shows the improved forecast model is effective and accurate.

一种改进的 Fuzzy c-means 聚类算法

该文提出了一种改进的ｆｕｚｚｙｃ－ｍｅａｎｓ算法（ＭＦＣＭ）。此算法是将传统算法（ＦＣＭ）直接对样本集聚类变为对特征集聚类，从而极大提高了ｆｕｚｚｙｃ－ｍｅａｎｓ的速度。证明了ＭＦＣＭ与ＦＣＭ在分类效果上的等价性，且ＭＦＣＭ较ＦＣＭ有较低的时间复杂性，讨论了ＭＦＣＭ与ＦＣＭ空间复杂性的关系。最后数值实验证实了结论。

一种新的基于Fuzzy c-means的高效自适应截集算法

提出了一种新的模糊聚类方法-自适应截集算法。该方法克服了聚类数目c要求预先确定、局部最优、分类不确定等弱点,对算法结构加以改进,增加聚类有效性问题的分析,在聚类过程中可动态调整聚类数目。针对时间消耗问题,利用模糊截集提高分类识别的速度。经实验表明,本算法可以提高聚类算法的可靠程度和分类识别的正确性。

一个FUZZY聚类分析的快速算法

Fuzzy聚类分析,是近年来在气象预报等很多科学领域中广泛应用的一种客观分析技术。本文根图的可迁闭包性质,探讨从模糊相似矩阵R中节点的可达性问题着手,生成相应的深度优先生成树(DFT)来完成聚类,从而得到一个时间复杂性为O(n^2)的快速Fuzzy聚类算法。

Clustering： from Clusters to Knowledge

Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities in intelligent data analyzing applications are mostly represented with the help of IF-THEN rules. With the help of these rules the following tasks are solved： prediction, classification, pattern recognition and others. Using different approaches---clustering algorithms, neural network methods, fuzzy rule processing methods--we can extract rules that in an understandable language characterize the data. This allows interpreting the data, finding relationships in the data and extracting new rules that characterize them. Knowledge acquisition in this paper is defined as the process of extracting knowledge from numerical data in the form of rules. Extraction of rules in this context is based on clustering methods K-means and fuzzy C-means. With the assistance of K-means, clustering algorithm rules are derived from trained neural networks. Fuzzy C-means is used in fuzzy rule based design method. Rule extraction methodology is demonstrated in the Fisher＇s Iris flower data set samples. The effectiveness of the extracted rules is evaluated. Clustering and rule extraction methodology can be widely used in evaluating and analyzing various economic and financial processes.

聚焦热度变化、主题动态与情感趋势的微博舆情演化研究

[研究目的]系统探讨微博舆情事件的演化特征,以提出针对性的对策建议,避免网络舆情扩散所可能引发的不利影响。[研究方法]为实现该目的,提出了基于CNN-BiLSTM-Attention的微博舆情多维特征演化分析框架,以深入剖析网络舆情的形成机制,进而优化对网络舆情的应对和处理策略。[研究结论]根据选取的事件从新浪微博获取数据,基于TF-IDF模型和K-Means聚类算法对微博舆情事件进行了维度划分,通过组合模型CNN-BiLSTM-Attention进行情感分类,并验证其准确性。最后,根据维度划分和情感分类的结果,结合舆情生命周期理论,从舆情热度、主题和情感三个方面研究了微博舆情事件的演化情况,并从生命周期和主题情感两方面得出网络舆情应对策略。

基于模糊理论的输电网络电压无功控制策略

为解决新能源、电动汽车、储能等新技术应用下,高复杂度电力系统电压稳定控制问题,提出了一种基于模糊理论的输电网络电压无功控制策略。该方法引入模糊理论中的隶属度函数,根据系统节点与不同分区之间的耦合程度制定无功控制策略。根据灵敏度计算网络各个节点之间的电气距离,通过模糊聚类算法对节点进行初步分区,并采用聚类融合算法对聚类产生的多个结果进行融合,从而得到最终分区结果。根据关键节点对各个分区的隶属度制定主辅控制策略。IEEE30节点输电网络的算例分析表明,该控制策略可以有效实现对无功功率的控制。

基于FCM和EO-SVM水轮机尾水管压力脉动特征识别

为有效识别水轮机尾水管压力脉动特征,提出了一种基于模糊C均值聚类、平衡优化器算法与支持向量机的识别方法。该方法首先采用平衡优化器算法优化SVM的惩罚因子和核函数以获得更好的SVM参数组合,构建EO-SVM识别模型以实现其在水轮机尾水管压力脉动特征识别中的应用。然后采用模糊C均值聚类算法将待分类的压力脉动特征进行初始聚类,将其分为四类,并依据聚类结果选择最靠近每类中心的样本作为EO-SVM模型的训练样本。将SVM和EO-SVM两种模型的识别分类结果进行比较,验证了所提EO-SVM模型的有效性。

主体-行为-客体语义分析构建技术功效矩阵

针对准确定义技术、功效主题的关键问题,通过分析技术、功效主题在构建技术功效矩阵中的语义共现性,提出一种基于主体-行为-客体语义分析的技术功效矩阵构建方法;基于目标领域制定检索表达式,在国家知识产权局专利数据库中下载相关专利信息数据,并预处理专利数据,得到目标专利信息文档;利用Python语言编程,采用中文分词工具包语言技术平台提取专利信息文档的主体-行为-客体语义结构,结合目标领域语料库、词频-逆文本频率和余弦相似度计算主题词的语义相似度;利用聚类算法Louvain算法实现社区网络划分,以凝练技术、功效主题,并通过主体-行为-客体语义结构的共现关系构建技术功效矩阵;以海底电缆反应力锥切削技术为例,通过专利实例分析验证所提出方法的有效性。结果表明:在分析大量专利实例以构建技术功效矩阵时,所提出的方法可以有效地实现专利实例中主体-行为-客体语义结构的社区网络划分;通过分析社区网络中节点主题的权重确定社区网络主题,提高了主题凝聚的准确性;在海底电缆反应力锥切削技术的专利实例分析中,利用主体-行为-客体语义结构和Louvain算法凝聚了7个技术主题、9个功效主题,验证了所提出方法的有效性。

智能制造系统可靠性与风险评估模型

针对智能制造系统的可靠性与风险评估问题,提出一种基于改进失效模式与影响分析(FMEA)的智能制造系统可靠性与风险评估模型。从创新运用组合权重、逼近理想解排序法思想和模糊多准则妥协解排序法的角度对传统FMEA模型进行改进;基于逼近理想解排序法思想得到专家权重,减少了专家团队对失效模式风险因子分析过程中的个体差异;使用模糊层次分析法和熵权法分别计算风险因子的主观和客观权重,减少了风险因子确定的主观性。最后,运用围绕中心点划分(PAM)聚类算法对改进模型得到的结果进行分析,并应用于智能制造系统风险评估中,确定了智能制造系统中各失效模式的重要程度,通过比较分析验证了改进模型的有效性。

面向电力营销的多源日志安全数据挖掘方法

针对当前电力营销业务系统内部电力营销数据分散、缺乏对电力营销数据统一管理,在多源电力营销数据库中应用了Apache Lucene的Elasticsearch分布式搜索引擎。通过采用主控芯片型号为XC7Z035FFGH676-2的Cortex-A9处理器,提高了电力营销多源电力营销安全数据信息的挖掘和计算能力;通过自组织映射神经网络与模糊聚类算法的聚类分析方法,提高了电力营销数据异常检测能力;利用自组织映射神经网络与模糊聚类算法减少能源数据消耗,提高了数据挖掘能力。所提方法的聚类分析时间最短为104 s,为下一步研究奠定了基础。

一种面向精细化地理分区的空间约束聚类方法

在空间分区的相关研究中,虽然已有经典聚类算法k均值聚类(k-means)结合空间约束的成果,但其对于连续平铺面状地理要素的空间聚类适用性不高。因此,本文开展对k-means算法进行空间约束的探讨。通过改进SKATER算法的空间约束方式,构建一种包含自然扩张与次优扩张过程的空间约束的k-means算法;并在两个公共数据集上与已有研究方法进行比较评价。结果表明:本文方法尤其适用于处理连续平铺面状地理要素的分区;通过轮廓系数、DB指数及总残差平方和三个评价指标知,本文方法优于已有的SKATER、AZP及SC k-means方法。研究成果不仅能够为地理信息系统中的空间数据处理提供新的工具,也为聚类算法的研究提供了新的视角。

基于改进模糊算法的节理分组软件开发

节理广泛存在于岩体中,其发育情况影响着岩体的稳定及渗流特性。由于节理数量众多,目前对其研究时需进行分组处理。传统的分组方法如依靠玫瑰图、极点等密度图等,无法确定每组节理的具体数据,同时对离散点的分组效果有限。当下使用机器学习的聚类算法也存在选择的聚类数影响分组效果的不足。鉴于此,在MATLAB平台上开发了基于改进模糊聚类算法的节理产状聚类程序(JOCP)。JOCP考虑节理的倾向倾角,使用基于聚拢度的模糊聚类算法进行分组,将结果使用Xie-beni指数判断优劣性,最终生成节理分组的最优解。JOCP以原始坐标数据及目标聚类数为输入,以节理产状数据、聚类中心、聚类结果分布图以及有效性指标为输出。将程序用于大连某边坡千条节理数据的分析中,结果证明程序可提高分组确定性,达到分组效果客观准确的目的。此程序可为地质勘探,灾害预测等领域提供技术支持。