2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)最新文献

A Graded Approach to Requirement Satisfaction for Evolving Systems 演进系统需求满足的分级方法

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2019-10-10 DOI: 10.1109/FUZZ-IEEE.2019.8858795

T. Martin, R. Anggraini

{"title":"A Graded Approach to Requirement Satisfaction for Evolving Systems","authors":"T. Martin, R. Anggraini","doi":"10.1109/FUZZ-IEEE.2019.8858795","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2019.8858795","url":null,"abstract":"There has been a strong trend towards autonomous and semi-autonomous systems in recent years. Evolving and adaptive systems embody the notion of autonomy, by changing their behavior (and possibly their structure) in response to changes in their environment. A consequence is that a designer may not be able to fully define the functional behavior of a system. Hence, formal verification and testing may not be possible. As a result, the self-adapting aspect of an evolving system is often implemented in an informal, ad hoc, manner and there is potential for causing significant harm if a system malfunctions in some way. A safety case requires more than an assertion that a system will work because it has not failed in testing. A more rigorous approach is essential, in which we can formally show that an evolving system meets its requirements and specifications. This paper outlines initial work in combining the X-mu approach (to model fuzzy uncertainty) with flexible requirements for an evolving system specified in RELAX, a formal framework to capture the uncertainty in evolving system requirements. A simple case study is used to illustrate some of the principles.","PeriodicalId":448018,"journal":{"name":"2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116595774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the Application of Preaggregation Functions to Fuzzy Pattern Tree 预聚集函数在模糊模式树中的应用

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2019-10-10 DOI: 10.1109/FUZZ-IEEE.2019.8858922

Pan Su, Tianhua Chen, Haoyu Mao, Jianyang Xie, Yitian Zhao, Jiang Liu

{"title":"On the Application of Preaggregation Functions to Fuzzy Pattern Tree","authors":"Pan Su, Tianhua Chen, Haoyu Mao, Jianyang Xie, Yitian Zhao, Jiang Liu","doi":"10.1109/FUZZ-IEEE.2019.8858922","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2019.8858922","url":null,"abstract":"Building transparent knowledge-based systems in the form of accurate and interpretable fuzzy rules is one of the significant applications of fuzzy set theory. The fuzzy connectives, i.e., T -norm/conorm, play the role of connecting fuzzy sets, which are essentially linguistic terms extracted from the knowledge embedded in a given data set. Fuzzy pattern tree is a recently proposed novel machine learning technique, which grows a hierarchical binary tree for each known class utilising conventional T -norms/conorms and aggregation operators. Preaggregation functions are recently proposed in the literature as a type of generalised aggregation functions, which have achieved successes in a number of applications. This paper proposes a preaggregation-based approach with application to the construction of fuzzy pattern tree. An experimental study is done to explore the performance of the fuzzy pattern tree where preaggregation functions are employed in comparison to that where conventional aggregation operators are utilised. Experimental results demonstrate that the performance of fuzzy pattern tree incorporated with the preaggregation function generated by Nilpotent minimum T -norm outperforms those with alternative preaggregation functions and the commonly used ordered weighted averaging operators.","PeriodicalId":448018,"journal":{"name":"2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129402431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Effective instance selection using the fuzzy-rough lower approximation 使用模糊粗糙下近似进行有效实例选择

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2019-10-10 DOI: 10.1109/FUZZ-IEEE.2019.8858799

Richard Jensen, M. Amiri, Neil MacParthaláin

引用次数: 1

A Study on the Interpretability of a Fuzzy System to Control an Inverted Pendulum 模糊系统控制倒立摆的可解释性研究

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2019-06-26 DOI: 10.1109/FUZZ-IEEE.2019.8858891

Bahadir Zeren, Muhammet Deveci, S. Coupland, R. John, E. Özcan

引用次数: 3

Learning Control of Tandem-Wing Tilt-Rotor UAV with Unsteady Aerodynamic Model 基于非定常气动模型的串联翼倾转旋翼无人机学习控制

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2019-06-23 DOI: 10.1109/FUZZ-IEEE.2019.8859023

Yunus Govdeli, S. Muzaffar, Raunak Raj, B. Elhadidi, E. Kayacan

引用次数: 1

Mining Fuzzy-Temporal Gradual Patterns 模糊时间渐变模式的挖掘

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2019-06-23 DOI: 10.1109/FUZZ-IEEE.2019.8858883

D. Owuor, Anne Laurent, J. Orero

引用次数: 14

Interval Type-2 fuzzy logic for dynamic parameter adjustment in the imperialist competitive algorithm 帝国主义竞争算法中动态参数调整的区间2型模糊逻辑

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2019-06-23 DOI: 10.1109/FUZZ-IEEE.2019.8858935

Emer Bernal, O. Castillo, J. Soria, F. Valdez

引用次数: 4

Clustering of Information Granules in Hotspot Identification 热点识别中信息粒的聚类

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2019-06-23 DOI: 10.1109/FUZZ-IEEE.2019.8858904

Yinghua Shen, W. Pedrycz, R. Moraes, Xingchen Hu, Xianmin Wang, A. Gacek

引用次数: 0

Ontology-Based Representation and Reasoning about Precise and Imprecise Time Intervals 基于本体的精确和不精确时间间隔表示与推理

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2019-06-23 DOI: 10.1109/FUZZ-IEEE.2019.8859019

F. Ghorbel, F. Hamdi, Elisabeth Métais

{"title":"Ontology-Based Representation and Reasoning about Precise and Imprecise Time Intervals","authors":"F. Ghorbel, F. Hamdi, Elisabeth Métais","doi":"10.1109/FUZZ-IEEE.2019.8859019","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2019.8859019","url":null,"abstract":"In this paper, we propose an ontology-based approach for representing and reasoning about precise and imprecise time intervals. This approach is three folds: (i) extending the 4D-fluents approach with new crisp and fuzzy components, to represent precise and imprecise time intervals, and temporal relations between them, (ii) extending the Allen’s interval algebra to enable reasoning about the handled temporal data, and (iii) creating a Fuzzy-OWL 2 ontology \"TimeOnto\" that, based on the extended Allen’s interval algebra, instantiate our 4D-fluents-based representation. The extension of the Allen’s algebra handles precise and imprecise time intervals to express precise (e.g., \"Before\") and imprecise (e.g., \"Just before\") temporal relations. Compared to related work, our imprecise relations are personalized, in the sense that they are not limited to a defined set of interval relations and their meanings are determined by the domain expert. For instance, the Allen’s relation \"Before\" may be generalized in 5 imprecise relations, where \"Before(1)\" means \"just before\" and gradually the time gap between the two intervals increases until \"Before(5)\" which means \"too long before\". To enable this representation, we propose an extension of the Vilain and Kautz’s point algebra and a redefinition of the Allen’s relations by means of this extended algebra. Unlike most related work, the resulting relations preserve many of the desirable properties of the Allen’s algebra. These relations can be used for temporal reasoning by means of a transitivity table. We describe a prototype based on TimeOnto that infers new relations using a set of SWRL and fuzzy IF-THEN rules. This prototype was integrated in an ontology-based memory prosthesis.","PeriodicalId":448018,"journal":{"name":"2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126401856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Improved Deep Fuzzy Clustering for Accurate and Interpretable Classifiers 用于精确和可解释分类器的改进深度模糊聚类

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Pub Date : 2019-06-23 DOI: 10.1109/FUZZ-IEEE.2019.8858809

Mojtaba Yeganejou, S. Dick

{"title":"Improved Deep Fuzzy Clustering for Accurate and Interpretable Classifiers","authors":"Mojtaba Yeganejou, S. Dick","doi":"10.1109/FUZZ-IEEE.2019.8858809","DOIUrl":"https://doi.org/10.1109/FUZZ-IEEE.2019.8858809","url":null,"abstract":"While deep learning has demonstrated excellent performance in many challenging learning tasks, it has not yet found broad acceptance amongst users. The key deficiency seems to be the uninterpretable nature of a deep neural network; no general, practical method for explaining the predictions or decisions of such a network has been devised. Lacking such, users seem unwilling to entrust deep learning with critical decisions.One approach to generating explanations is to design algorithms that are inherently more interpretable. Neuro-fuzzy systems are an example, which we are extending to deep networks; in particular by designing deep fuzzy clustering algorithms. Deep fuzzy clustering employs a deep learner as an automated feature extractor. A fuzzy clustering is performed in the extracted feature space, and a classifier built from it. The resulting model appears more interpretable, but at the cost of lower accuracy. This paper explores improvements to deep fuzzy clustering leading to a more accurate deep fuzzy classifier that still seems highly interpretable. We evaluate the accuracy and interpretability of the model on the MNIST dataset.","PeriodicalId":448018,"journal":{"name":"2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125761104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9