检索结果-南通市图书馆

Novel moderate transformation of fuzzy membership function into basic belief assignment

Chinese Journal of Aeronautics 2023年第1期36卷 369-385页

作者： Xiaojing FAN Deqiang HAN Jean DEZERT Yi YANG School of Automation Science and Engineering Xi’an Jiaotong UniversityXi’an 710049China ONERA The French Aerospace LabPalaiseau 91761France SKLSVMS School of AerospaceXi’an Jiaotong UniversityXi’an 710049China

In information fusion,the uncertain information from different sources might be modeled with different theoretical frameworks.When one needs to fuse the uncertain information represented by different uncertainty theories,constructing the transformation between different frameworks is crucial.Various transformations of a fuzzy membership function(FMF)into a Basic Belief Assignment(BBA)have been proposed,where the transformations based on uncertainty maximization and minimization can determine the BBA without preselecting the focal elements.However,these two transformations that based on uncertainty optimization emphasize the extreme cases of uncertainty.To avoid extreme attitudinal bias,a trade-off or moderate BBA with the uncertainty degree between the minimal and maximal ones is more preferred.In this paper,two moderate transformations of an FMF into a trade-off BBA are proposed.One is the weighted average based transformation and the other is the optimization-based transformation with weighting mechanism,where the weighting factor can be user-specified or determined with some prior information.The rationality and effectiveness of our transformations are verified through numerical examples and classification examples.

关键词： Basic belief assignment Belief functions fuzzy membership function Information fusion Moderate transformation

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Track-to-track association using fuzzy membership function and clustering for distributed information fusion

Track-to-track association using fuzzy membership function a...

引用

第37届中国控制会议

作者： Shuaixin Hong Dongliang Peng Yifang Shi Automation School Hangzhou Dianzi University

In distributed information fusion application, developing an efficient track-to-track association approach becomes crucially important which may significantly benefit the sequent track-to-track fusion procedure. This paper proposes a novel track-to-track association method specialized for distributed multitarget tracking using more than two sensors. In order to mathematically interpret how probable that the two tracks from two different sensors are tracking the same targets, the fuzzy membership of the two tracks are calculated, whose value is between 0 and 1, with bigger values indicating higher probability that the two tracks originate from the same target. Based on the calculated fuzzy membership matrix, the clustering methodology is then utilized to pick out the group of tracks tracking the same target. The simulation results validate the efficiency and superiority over the existing approach.

关键词： Distributed information track-to-track association fuzzy membership function clustering

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Research on trend prediction of component stock in fuzzy time series based on deep forest

引用

CAAI Transactions on Intelligence Technology 2022年第4期7卷 617-626页

作者： Peng Li Hengwen Gu Lili Yin Benling Li School of Computer and Science and Technology Harbin University of Science and TechnologyHarbinChina Department of Military Industry Development China Shipbuilding Industry Corporation 703 Research InstituteHarbinChina

With the continuous development of machine learning and the increasing complexity of financial data analysis,it is more popular to use models in the field of machine learning to solve the hot and difficult problems in the financial industry.To improve the effectiveness of stock trend prediction and solve the problems in time series data processing,this paper combines the fuzzy affiliation function with stock-related technical indicators to obtain nominal data that can widely reflect the constituent stocks in the case of time series changes by analysing the S&P 500 index.Meanwhile,in order to optimise the current machine learning algorithm in which the setting and adjustment of hyperparameters rely too much on empirical knowledge,this paper combines the deep forest model to train the stock data separately.The experimental results show that(1)the accuracy of the extreme random forest and the accuracy of the multi-grain cascade forest are both higher than that of the gated recurrent unit(GRU)model when the un-fuzzy index-adjusted dataset is used as features for input,(2)the accuracy of the extreme random forest and the accuracy of the multigranular cascade forest are improved by using the fuzzy index-adjusted dataset as features for input,(3)the accuracy of the fuzzy index-adjusted dataset as features for inputting the extreme random forest is improved by 18.89% compared to that of the un-fuzzy index-adjusted dataset as features for inputting the extreme random forest and(4)the average accuracy of the fuzzy index-adjusted dataset as features for inputting multi-grain cascade forest increased by 5.67%.

关键词： deep forest fuzzy membership function price pattern time series trend forecast

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Geometric Mean Maximum FSVMI Model and Its Application in Carotid Artery Stenosis Risk Prediction

引用

Chinese Journal of Electronics 2021年第5期30卷 824-832页

作者： ZHANG Xueying GUO Yuling LI Fenglian WEI Xin HU Fengyun HUI Haisheng JIA Wenhui College of Information and Computer Taiyuan University of Technology Department of Neurology Shanxi Province People's Hospital

Carotid artery stenosis is a serious medical condition that can lead to stroke. Using machine learning method to construct classifier model, carotid artery stenosis can be diagnosed with transcranial doppler data. We propose an improved fuzzy support vector machine model to predict carotid artery stenosis, with the maximum geometric mean as the optimization target.The fuzzy membership function is obtained by combining information entropy with the normalized class-center distance. Experimental results showed that the proposed model was superior to the benchmark models in sensitivity and geometric mean criteria.

关键词： Optimisation techniques Computer vision and image processing techniques Data handling techniques Other topics in statistics Combinatorial mathematics Patient diagnostic methods and instrumentation Biology and medical computing Carotid artery stenosis risk prediction Imbalanced TCD dataset fuzzy membership function Geometric mean maximum optimization target fuzzy support vector machine with imbalanced regulator model

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Delayed recognition: recent developments and a proposal to study this phenomenon as a fuzzy concept

引用

Journal of Data and Information Science 2018年第3期3卷 1-13页

作者： Ronald Rousseau University of Antwerp Faculty of Social Sciences B-2020 Antwerp Belgium d KU LeuvenFacultair Onderzoekscentrum ECOOM

Purpose： New developments in the study of delayed recognition are discussed.Design/methodology/approach： Based on these new developments a method is proposed to characterize delayed recognition as a fuzzy concept.Findings： A benchmark value of 0.333 corresponding with linear growth is obtained. Moreover, a case is discovered in which an expert found delayed recognition several years before citation analysis could discover this phenomenon. Research limitations： As all citation studies also this one is database dependent.Practical implications： Delayed recognition is turned into a fuzzy concept.Originality/value： The article presents a new way of studying delayed recognition.

关键词： Delayed recognition Sleeping beauty Hibernators fuzzy membership function

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Kernel Density Estimation Based Multiphase fuzzy Region Competition Method for Texture Image Segmentation

引用

Communications in Computational Physics 2010年第8期8卷 623-641页

作者： Fang Li Michael K.Ng Department of Mathematics East China Normal UniversityShanghai 200241China Centre for Mathematical Imaging and Vision and Department of Mathematics Hong Kong Baptist UniversityKowloon TongHong Kong

In this paper,we propose a multiphase fuzzy region competition model for texture image segmentation.In the functional,each region is represented by a fuzzy membership function and a probability density function that is estimated by a nonparametric kernel density estimation.The overall algorithm is very efficient as both the fuzzy membership function and the probability density function can be implemented easily.We apply the proposed method to synthetic and natural texture images,and synthetic aperture radar images.Our experimental results have shown that the proposed method is competitive with the other state-of-the-art segmentation methods.

关键词： Texture multiphase region competition kernel density estimation fuzzy membership function total variation

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：