Global Optimization Method Using SLE and Adaptive RBF Based on Fuzzy Clustering

High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis models are so computationally expensive that the time required in design optimization is usually unacceptable.In order to improve the efficiency of optimization involving high fidelity analysis models,the optimization efficiency can be upgraded through applying surrogates to approximate the computationally expensive models,which can greately reduce the computation time.An efficient heuristic global optimization method using adaptive radial basis function（RBF） based on fuzzy clustering（ARFC） is proposed.In this method,a novel algorithm of maximin Latin hypercube design using successive local enumeration（SLE） is employed to obtain sample points with good performance in both space-filling and projective uniformity properties,which does a great deal of good to metamodels accuracy.RBF method is adopted for constructing the metamodels,and with the increasing the number of sample points the approximation accuracy of RBF is gradually enhanced.The fuzzy c-means clustering method is applied to identify the reduced attractive regions in the original design space.The numerical benchmark examples are used for validating the performance of ARFC.The results demonstrates that for most application examples the global optima are effectively obtained and comparison with adaptive response surface method（ARSM） proves that the proposed method can intuitively capture promising design regions and can efficiently identify the global or near-global design optimum.This method improves the efficiency and global convergence of the optimization problems,and gives a new optimization strategy for engineering design optimization problems involving computationally expensive models.

Refracturing candidate selection for MFHWs in tight oil and gas reservoirs using hybrid method with data analysis techniques and fuzzy clustering

The selection of refracturing candidate is one of the most important jobs faced by oilfield engineers. However, due to the complicated multi-parameter relationships and their comprehensive influence, the selection of refracturing candidate is often very difficult. In this paper, a novel approach combining data analysis techniques and fuzzy clustering was proposed to select refracturing candidate. First, the analysis techniques were used to quantitatively calculate the weight coefficient and determine the key factors. Then, the idealized refracturing well was established by considering the main factors. Fuzzy clustering was applied to evaluate refracturing potential. Finally, reservoirs numerical simulation was used to further evaluate reservoirs energy and material basis of the optimum refracturing candidates. The hybrid method has been successfully applied to a tight oil reservoir in China. The average steady production was 15.8 t/d after refracturing treatment, increasing significantly compared with previous status. The research results can guide the development of tight oil and gas reservoirs effectively.

Kernel method-based fuzzy clustering algorithm

The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.

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.

AN ANALYSIS OF THE APPLICABILITY OF FUZZY CLUSTERING IN ESTABLISHING AN INDEX FOR THE EVALUATION OF METEOROLOGICAL SERVICE SATISFACTION

An evaluation index is a prerequisite for the scientific evaluation of a public meteorological service.This paper aims to explore a technical method for determining and screening evaluation indicators.Based on public satisfaction survey data obtained in Wafangdian,China in 2010,this study investigates the suitability of fuzzy clustering analysis method in establishing an evaluation index.Through quantitative analysis of multilayer fuzzy clustering of various evaluation indicators,correlation analysis indicates that if the results of clustering were identical for two evaluation indicators in the same sub-evaluation layer,then one indicator could be removed,or the two indicators merged.For evaluation indicators in different sub-evaluation layers,although clustering reveals attribute correlations,these indicators may not be substituted for one another.Analysis of the applicability of the fuzzy clustering method shows that it plays a certain role in the establishment and correction of an evaluation index.

Modified possibilistic clustering model based on kernel methods

A novel model of fuzzy clustering using kernel methods is proposed. This model is called kernel modified possibilistic c-means （KMPCM） model. The proposed model is an extension of the modified possibilistic c-means （MPCM） algorithm by using kernel methods. Different from MPCM and fuzzy c-means （FCM） model which are based on Euclidean distance, the proposed model is based on kernel-induced distance. Furthermore, with kernel methods the input data can be mapped implicitly into a high-dimensional feature space where the nonlinear pattern now appears linear. It is unnecessary to do calculation in the high-dimensional feature space because the kernel function can do it. Numerical experiments show that KMPCM outperforms FCM and MPCM.

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.

Improvements to the fuzzy mathematics comprehensive quantitative method for evaluating fault sealing

Fuzzy mathematics is an important means to quantitatively evaluate the properties of fault sealing in petroleum reservoirs.To accurately study fault sealing,the comprehensive quantitative evaluation method of fuzzy mathematics is improved based on a previous study.First,the single-factor membership degree is determined using the dynamic clustering method,then a single-factor evaluation matrix is constructed using a continuous grading function,and finally,the probability distribution of the evaluation grade in a fuzzy evaluation matrix is analyzed.In this study,taking the F1 fault located in the northeastern Chepaizi Bulge as an example,the sealing properties of faults in different strata are quantitatively evaluated using both an improved and an un-improved comprehensive fuzzy mathematics quantitative evaluation method.Based on current oil and gas distribution,it is found that our evaluation results before and after improvement are significantly different.For faults in＂best＂and＂poorest＂intervals,our evaluation results are consistent with oil and gas distribution.However,for the faults in＂good＂or＂poor＂intervals,our evaluation is not completelyconsistent with oil and gas distribution.The improved evaluation results reflect the overall and local sealing properties of target zones and embody the nonuniformity of fault sealing,indicating the improved method is more suitable for evaluating fault sealing under complicated conditions.

Clustering Analysis of the Basic Structure of Relevant Community Service Organizations in Cities in China

With the gradually development of economy in China, people＇s living stan- dards have been improved, which makes people have higher and higher require- ments on the quality of life, and thus community service has become and essential part in people＇s life. In order to understand the basic building blocks of community service organizations in different cities in China, classification comparison was made to the data of 31 cities in China from China Statistical Year Book （2014） by using SPSS clustering method and the fuzzy clustering method, so as to find out the dif- ferences and the causes of the differences, with the aim to promote the manage- ment of relevant government and personnel.

Kernel Generalized Noise Clustering Algorithm

To deal with the nonlinear separable problem, the generalized noise clustering （GNC） algorithm is extended to a kernel generalized noise clustering （KGNC） model. Different from the fuzzy c-means （FCM） model and the GNC model which are based on Euclidean distance, the presented model is based on kernel-induced distance by using kernel method. By kernel method the input data are nonlinearly and implicitly mapped into a high-dimensional feature space, where the nonlinear pattern appears linear and the GNC algorithm is performed. It is unnecessary to calculate in high-dimensional feature space because the kernel function can do it just in input space. The effectiveness of the proposed algorithm is verified by experiments on three data sets. It is concluded that the KGNC algorithm has better clustering accuracy than FCM and GNC in clustering data sets containing noisy data.

Fault Pattern Recognition based on Kernel Method and Fuzzy C-means

A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means （FCM） is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery.

分布式模糊聚类微动法铁路路基岩溶地球物理探测:以皖赣铁路宁国改线工程为例

微动勘探法可探查铁路路基地下岩溶、裂隙通道等不良地质体的发育位置,针对反演成果中土、岩体分界面模糊不清,异常位置及边界不准确等问题,采用分布式模糊聚类算法分析反演数据。系统回顾了微动勘探法和分布式模糊聚类算法基本原理,以皖赣铁路宁国改线某区间既有铁路路基岩溶勘察为例,开展分布式模糊聚类微动勘探进行地层分层、溶洞自动划分。将分布式模糊聚类法分析前后的反演数据同时与钻探揭露结果对比发现,分布式模糊聚类算法可对分界面、异常区域进行自动有效划定,可更加准确地识别地质异常体。说明该方法可较大程度提高微动反演数据的准确率,为铁路路基工程的设计和施工提供参考。

基于Fuzzy-AHP的采矿方法优选辅助系统开发与应用

采矿方法选择是矿山生产设计中的重要环节,如何便捷地优选采矿方法是矿山设计人员一直关心的问题。将采矿方法优选视为一个多目标模糊优化问题,运用模糊综合理论与层次分析法,建立了采矿方法优选的Fuzzy-AHP模型。然后,运用VB编程,开发了一套具有数据存储功能、运算便捷、操作简单和可视化程度高的计算机辅助优选系统。最后应用该系统对某硫铁矿采矿方法进行了优选,取得了良好的工程效果。实践表明,该系统为矿山采矿方法优选提供了一套可靠性高的辅助决策工具。

Track-Pattern-Based Characteristics of Extratropical Transitioning Tropical Cyclones in the Western North Pacific

Based on the Regional Specialized Meteorological Center(RSMC)Tokyo-Typhoon Center best-track data and the NCEP-NCAR reanalysis dataset,extratropical transitioning(ET)tropical cyclones(ETCs)over the western North Pacific(WNP)during 1951–2021 are classified into six clusters using the fuzzy c-means clustering method(FCM)according to their track patterns.The characteristics of the six hard-clustered ETCs with the highest membership coefficient are shown.Most tropical cyclones(TCs)that were assigned to clusters C2,C5,and C6 made landfall over eastern Asian countries,which severely threatened these regions.Among landfalling TCs,93.2%completed their ET after landfall,whereas 39.8%of ETCs completed their transition within one day.The frequency of ETCs over the WNP has decreased in the past four decades,wherein cluster C5 demonstrated a significant decrease on both interannual and interdecadal timescales with the expansion and intensification of the western Pacific subtropical high(WPSH).This large-scale circulation pattern is favorable for C2 and causes it to become the dominant track pattern,owning to it containing the largest number of intensifying ETCs among the six clusters,a number that has increased insignificantly over the past four decades.The surface roughness variation and three-dimensional background circulation led to C5 containing the maximum number of landfalling TCs and a minimum number of intensifying ETCs.Our results will facilitate a better understanding of the spatiotemporal distributions of ET events and associated environment background fields,which will benefit the effective monitoring of these events over the WNP.

考虑数据分类的建筑电能耗集成预测方法

建筑侧各类可再生能源的应用日益普及,建筑电能耗预测在用能供需平衡、电网稳定运行、尖峰需求响应等方面发挥越来越重要作用。尽管诸多数据驱动模型在能耗预测方面获得广泛应用,当前仍缺乏预测精度高、泛化能力强的短期预测模型。针对该问题,提出一种基于建筑物能耗特点并结合数据挖掘技术的分类集成式能耗预测方法。首先,采用递归特征消除法对数据进行特征筛选,并用模糊C均值聚类算法对训练集数据进行聚类,使用K最邻近法对验证集和测试集数据进行归类;选择5种结合智能优化算法的混合数据驱动模型作为子学习器,分别对每类数据做预测,最后使用多元线性回归法进行结果集成。经3个建筑电力用能案例验证,此集成预测模型精度均优于单个子模型,具有适用不同建筑类型和用能尺度的预测潜力。

基于重叠社区发现的网络数据可视化优化方法研究与实现

伴随数据的迅猛增长,数据间关系变得错综复杂,给网络数据可视化带来了挑战。通过社区发现,凸显网络中的局部聚类特性可以提高可视化效果,而重叠社区的发现更贴近现实中的网络结构。具有简单高效执行速度快的Louvain算法是目前最常用的社区发现算法之一,但重叠社区的发现是其不足之处。为此,论文以Louvain算法为基础,结合基于谱映射的模糊C-means聚类算法改进社区发现算法,改进的算法利用谱映射将数据节点映射到欧几里得空间,以隶属度计算数据节点属于某个聚类的程度,由此可以允许同一数据属于多个不同的类,从而实现重叠社区结构的发现,最后基于所提出改进算法,使用主流布局算法中的FR模型对网络数据进行可视化。以模块度值作为评估指标,实验结果表明,论文提出的方法能够发现重叠社区,可以清晰地展示网络中的社区结构,在经典数据集上与传统重叠社区发现算法COPRA和CPM相比,模块度值得到提高。

基于Neuro-Fuzzy方法的Web服务器访问流量预测

Neuro Fuzzy方法是将神经网络和模糊逻辑有机的结合 ,用于解决复杂的非线性问题 ;用它来进行Web服务器流量预测 ,是一种新的思路和方法 .主要介绍了模型构造的基本思想、结构。

FUZZY ISODATA聚类法在地下水水化学分类中的应用

本文以杜热草场水化学资料为例,运用FUZZY ISODATA聚类方法对地下水水化学类型的划分进行了初步研究,并与传统的舒卡列夫法和基于模糊关系聚类法所得结果进行了对比,说明了本方法的可靠性。文中运用该法对研究区水化学成份划分的五种类型,基本符合本区地下水化学成份形成与分布规律,分类合理,计算简便,特别是对水化学成份差别不大的地区更为适用。

基于Neuro-Fuzzy方法的Web服务器访问流量预测

Neuro-Fuzzy方法是将神经网络和模糊逻辑进行有机的结合，用于解决复杂的非线性问题；用它来进行Web服务器流量预测，是一种新的思路和方法。该文介绍了模型构造的基本思想、结构、算法，也介绍了进化式聚类方法和预测过程；同时，给出了实验数据及分析。

基于模糊聚类法的光伏配电网负荷过载预测

配电网负荷过载对运行安全性造成消极影响,且光伏配电网负荷过载预测时,受天气影响,导致预测误差较大。为此,基于模糊聚类法提出一种新的光伏配电网负荷过载预测方法。分析光伏配电网因素,通过模糊聚类实现信息处理,确定聚类中心,分析样本相似度,根据分析结果完成分类识别。采用路径分析方法分析影响因素指标权重,计算权重系数。利用替代法通过构造新特征加快拟合速度,通过BP神经网络优化参数,完成配电网负荷过载预测。实验结果表明,所提方法在恶劣环境下预测误差低于5%,具有较强的抗干扰能力。