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.

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.

Image Segmentation Based on Intuitionistic Type-2 FCM Algorithm

Due to using the fuzzy clustering algorithm,the accuracy of image segmentation is not high enough.So one hybrid clustering algorithm combined with intuitionistic fuzzy factor and local spatial information is proposed.Experimental results show that the proposed algorithm is superior to other methods in image segmentation accuracy and improves the robustness of the algorithm.

IFCEM based recognition method for target with interval-overlapped hybrid attributes

When the attributes of unknown targets are not just numerical attributes,but hybrid attributes containing linguistic attributes,the existing recognition methods are not effective.In addition,it is more difficult to identify the unknown targets densely distributed in the feature space,especially when there is interval overlap between attribute measurements of different target classes.To address these problems,a novel method based on intuitionistic fuzzy comprehensive evaluation model(IFCEM)is proposed.For numerical attributes,targets in the database are divided into individual classes and overlapping classes,and for linguistic attributes,continuous interval-valued linguistic term set(CIVLTS)is used to describe target characteristic.A cloud modelbased method and an area-based method are proposed to obtain intuitionistic fuzzy decision information of query target on numerical attributes and linguistic attributes respectively.An improved inverse weighted kernel fuzzy c-means(IWK-FCM)algorithm is proposed for solution of attribute weight vector.The possibility matrix is applied to determine the identity and category of query target.Finally,a case study composed of parameter sensitivity analysis,recognition accuracy analysis.and comparison with other methods,is taken to verify the superiority of the proposed method.

二氧化氮浓度时空预测:一种区间二型直觉模糊神经网络方法

空气中二氧化氮浓度的高低对环境保护和公共健康具有重要影响。目前二氧化氮浓度预测方法在表征时空关联性方面存在不足。鉴于此,提出了新的使用区间二型直觉模糊神经网络时空预测二氧化氮浓度的方法。首先,阐述了该区间二型直觉模糊神经网络框架,引入可变系数加权其隶属部分和非隶属部分的输出,并采用随机向量泛函链接神经网络作为规则后件;然后,为确定网络结构和参数,采用分层聚类算法得到模糊规则库,并通过最小二乘法优化网络后件的输出权值;最后,使用2018年1月至3月采集的北京市二氧化氮浓度真实数据进行数值验证。实验结果表明,与现有方法相比,该方法在短期和长期时空预测方面均取得了较高的预测精度和效率。

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.

基于内点法改进的直觉模糊C均值分割算法

传统的模糊C均值(Fuzzy C-Means, FCM)算法求解隶属度函数和聚类中心表达式时,未充分利用算法的约束条件,导致得到的隶属度函数未收敛到最优,降低了目标像素和邻域像素与聚类中心之间的依赖,影响了图像分割的效果。针对上述问题,采用内点法的思想设计惩罚函数,将惩罚函数引入FCM算法的目标函数中,再利用KKT条件求出全约束条件下的隶属度。然后将模糊集推广到直觉模糊集,引入非隶属度和犹豫度,进一步优化隶属度矩阵,完善图像中的不确定信息。分割实验表明,改进后的算法既增强了抗噪性,又保护了图像的细节。相比于FCM算法本文算法的分割准确率至少提高了3.11%、划分系数提高了9.29%;对比于FCM_S2算法,上述算法的分割准确率提高了约0.5%,划分系数提高了2.14%。

基于TOPSIS方法的明挖基坑风险识别

针对深基坑工程施工过程中潜在风险因素众多而难以识别的问题,提出基于直觉模糊优劣解距离法(TOPSIS)的多属性评价方法对风险因素进行评估。首先,基于基坑安全事故案例、专家工程经验和实际工程,分析并确定潜在的风险因素;进而,根据风险因素构建风险评价体系,应用直觉模糊数确定专家和评价准则的权重;最后,运用TOPSIS多属性决策方法识别和评估潜在的风险因素。最终通过实例验证了该方法的可行性。研究表明:本方法可有效识别施工过程的高风险因素,可作为基坑施工安全风险分析与控制的决策工具。

基于多准则直觉模糊的服装企业绿色供应商选择

在“双碳”背景下,服装企业由于其原料和生产工艺特点,更需要注重绿色环保,但目前服装企业在供应商选择中少有考虑这些因素。为此,基于绿色供应链管理,在供应商选择评价准则中设计绿色评价准则,并根据准则的语言变量特性,采用直觉模糊环境下的多准则妥协解排序(VIKOR)决策方法选择最优供应商。基于偏好度不同重新设计决策者权重的信任函数计算方法,使得不同类决策者评价对整体评价的作用更加合理。实例分析、敏感性分析以及逼近理想解排序(TOPSIS)方法对比分析表明,该方法能有效地实现服装企业绿色供应商优选。

Dynamic assessment method of air target threat based on improved GIFSS

As the air combat environment becomes more complicated and changeable, accurate threat assessment of air target has a significant impact on air defense operations. This paper proposes an improved generalized intuitionistic fuzzy soft set (GIFSS) method for dynamic assessment of air target threat. Firstly, the threat assessment index is reasonably determined by analyzing the typical characteristics of air targets. Secondly, after the GIFSS at different time is obtained, the index weight is determined by the intuitionistic fuzzy set entropy and the relative entropy theory. Then, the inverse Poisson distribution method is used to determine the weight of time series, and then the time-weighted GIFSS is obtained. Finally, threat assessment of five air targets is carried out by using the improved GIFSS (I-GIFSS) and comparison methods. The validity and superiority of the proposed method are verified by calculation and comparison.

An Interactive Intuitionistic Fuzzy Method for Multilevel Linear Programming Problems

In this paper, we propose an interactive method for solving the multilevel linear programming problems based on the intuitionistic fuzzy set theory. Firstly, the membership function and the non-membership function are introduced to describe the uncertainty of the decision makers. Secondly, a satisfactory solution is derived by updating the minimum satisfactory degrees with considerations of the overall satisfactory balance among all levels. In addition, the steps of the proposed method are given in this paper. Finally, numerical examples illustrate the feasibility of this method.

基于知识量加权的直觉模糊均值聚类方法

针对聚类算法中特征数据对聚类中心贡献的差异性及算法对初始聚类中心的敏感性等问题,提出一种基于知识量加权的直觉模糊均值聚类方法。首先将原始数据集直觉模糊化并改进最新的直觉模糊知识测度计算知识量,据此实现数据集特征加权,再利用核空间密度与核距离初始化聚类中心,以提高高维特征数据集的计算精度与聚类效率,最后基于类间样本距离与最小知识量原理建立聚类优化模型,得到最优迭代算法。基于UCI人工数据集的实验结果表明,所提方法较大程度地提高了聚类的准确性与迭代效率,分类正确率及执行效率分别平均提高了10.63%和31.75%,且具有良好的普适性和稳定性。该方法首次将知识测度新理论引入模糊聚类并取得优良效果,为该理论在其他相关领域的潜在应用开创了新例。

基于改进直觉模糊熵与Dice测度的VIKOR应急决策

针对不确定信息环境下决策属性权重未知的应急决策问题,提出一种改进直觉模糊熵权与Dice距离测度的VIKOR决策模型.首先,分析现有直觉模糊熵存在的不足,提出一种考虑隶属度、非隶属度以及犹豫度综合影响的改进直觉模糊熵;其次,引入Dice系数,结合犹豫度的特点构建直觉模糊Dice距离测度;然后,将直觉模糊熵权与直觉模糊Dice距离测度运用到基于直觉模糊的VIKOR方法中,并定义了群体贴近度公式,进而构建契合现实环境的应急决策模型;最后通过地震灾害后的应急决策算例,验证了该模型的合理性和有效性.

基于直觉模糊—熵权—TOPSIS的农业面源污染防治技术综合评价方法研究

研究提出了一种基于直觉模糊-熵权-TOPSIS的农业面源污染防治技术评估方法,在TOPSIS技术框架基础上,耦合直觉模糊集理论和直觉模糊熵权法,同时实现了对定性指标的定量转换和客观赋权;以典型种植业面源污染防治技术为例,开展方法应用验证,分别从源头类、过程类、末端类和全过程类技术中,筛选出综合效益较高的农业面源污染防治技术,从环境、经济、技术性能等不同维度深入剖析了各项技术的优势与不足。结果表明,该方法能够克服定性数据和主观赋权对评价结果的影响,综合考虑农业面源污染防治技术的环境效益、经济效益和技术性能,实现技术综合评价与优选,并为技术未来的改进方向提供科学指导。

基于三角直觉模糊数的企业开源情报产品质量评价研究

[目的/意义]信息时代开源情报发挥的作用日益显著,完善开源情报产品质量评价体系有利于创新型企业决策支撑和竞争发展。[方法/过程]将基于三角直觉模糊数的TODIM方法应用情报质量评价中。首先,利用模糊DEMATEL方法解决评价体系指标之间相互影响并进行客观赋权;其次,结合TODIM评价方法对开源情报产品质量进行排序优选;最后,结合哈尔滨市信息技术软件开发公司的实证研究探讨质量评价方法可行性。[结果/结论]通过与其他评价方法的结果进行对比分析,验证了利用三角直觉模糊数建立模型的有效性和实用性,符合开源情报产品质量评价的实际需求。考虑评价过程中决策者风险规避的心理行为,也为情报产品质量评价提供新的思考维度。

考虑风险偏好行为的铁路选线决策模型研究

针对传统铁路选线决策模型中未考虑决策者风险偏好行为对决策结果产生的影响,以及定量指标和定性指标难以综合评价的系统量化问题,选用技术可行性、经济合理性、施工及环境影响和社会政治经济意义4个方面的影响因素,建立铁路选线决策指标体系,并采用九级标度语言评价值对定性指标进行评价,再将定性指标转换为直觉模糊数带入计算;然后定义效用函数表达决策者风险偏好行为,采用两次改进熵权法确定指标客观权重;最后结合基于对称交互熵的多属性决策排序法,构建一种新的考虑决策者风险偏好行为的铁路选线决策模型。根据该模型,通过选择不同的风险偏好因子k得出与其对应的最优方案。通过案例分析,该模型具有更好的实践性和可靠性,为今后类似风险偏好铁路选线决策工作提供了一种新的应对策略。

基于直觉模糊环境下汽车行业的绿色供应商选择研究

针对汽车企业绿色供应商选择过程中的不确定性问题,文中在直觉模糊环境下采用VIKOR决策方法。首先,采用直觉模糊数表示供应商的评价指标,从而处理评价信息的不确定性;其次,利用直觉模糊熵理论计算指标权重与专家权重,把犹豫度纳入考虑,增加评价的准确性;最后,依据VIKOR方法对决策方案进行择优排序,并通过敏感性分析,考虑专家偏好情况,提高决策方法的科学性。

基于GIS和区间直觉模糊集的铁路线网规划评价

为了综合全面地反映城市群高速铁路规划线网的总体性能,评价比选出最优规划方案,针对复杂多因素约束条件下评价指标信息的非“二分法”特性,本文在GIS技术支持下,尝试提出了基于区间直觉模糊集的城市群高速铁路线网评价模型。该模型从区间直觉模糊数的几何意义出发,基于熵值最大化原理,求出指标的权重,获得不同决策者对各个方案的评价值。最后,综合不同决策者意见得出各个方案的最终评价值。以辽中南城市群高速铁路线网规划评价为例,进行了基于区间直觉模糊集的城市群高速铁路线网方案评价。分析表明提出的方法能够描述决策者判断过程中的犹豫程度,可以更细腻、更全面地描述评价指标的自然属性,从而使评价结果更符合客观实际情况。

基于模糊TOPSIS法的高速公路隧道运营安全评价模型

为了丰富高速公路隧道运营安全评价方法,首先从交通环境、隧道结构、安全管理、机电设施四个维度构建了隧道安全运营评价指标体系,并将隧道运营安全等级分为4个层次。其次通过直觉模糊层次分析法确定各指标的权重和分级评价矩阵,利用TOPSIS决策方法对隧道运营安全等级进行评价。最后根据三明莆炎高速隧道监测数据进行评价,得出隧道运营安全等级为一级,同时将该方法与传统模糊层次分析法进行对比,结果表明新方法在评价区分度和可靠度方面改进显著。

基于直觉模糊n-cell数的多属性决策方法及应用

针对权重已知的直觉模糊多属性决策问题,提出了一种基于直觉模糊n-cell数的决策方法。定义了直觉模糊n-cell数的偏序和两种基于均值和离散度的弱序。研究了所提出序的理论性质,并进行比较分析。最后,将相关排序方法应用于一类人才选拔的问题之中,在解决问题的同时验证了方法的有效性。