Fuzzy Sets and Systems最新文献_第8页

Interactive calculus: Theory and applications of A-linearly correlated bivariate fuzzy processes 交互演算：a -线性相关二元模糊过程的理论与应用

IF 2.7 1区数学

Fuzzy Sets and Systems Pub Date : 2026-04-01 Epub Date: 2025-11-29 DOI: 10.1016/j.fss.2025.109699

L.C. Barros , M. Shahidi , E. Esmi , T. Allahviranloo

引用次数: 0

Beyond parity: A fuzzy life-cycle index of gender equality with legal–Practical implementation gaps 超越平等：具有法律实践执行差距的性别平等模糊生命周期指数

IF 2.7 1区数学

Fuzzy Sets and Systems Pub Date : 2026-04-01 Epub Date: 2025-12-03 DOI: 10.1016/j.fss.2025.109717

Firas Kaabi

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A sufficient and necessary condition of coincidence of the concave integral and the pan-integral 凹积分与泛积分重合的充要条件

IF 2.7 1区数学

Fuzzy Sets and Systems Pub Date : 2026-04-01 Epub Date: 2025-12-12 DOI: 10.1016/j.fss.2025.109728

Tong Kang , Jun Li , Leifan Yan , Ran Wang

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Quasi-deterministic fuzzy automata: Isomorphisms and fuzzy deterministic automata minimization 准确定性模糊自动机：同构与模糊确定性自动机最小化

IF 2.7 1区数学

Fuzzy Sets and Systems Pub Date : 2026-03-15 Epub Date: 2025-11-16 DOI: 10.1016/j.fss.2025.109677

José R. González de Mendívil , Zorana Jančić , Aitor González de Mendívil Grau , Ivana Micić , Stefan Stanimirović

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引用次数: 0

Extreme mass distributions for quasi-copulas 准联结的极端质量分布

IF 2.7 1区数学

Fuzzy Sets and Systems Pub Date : 2026-03-15 Epub Date: 2025-11-22 DOI: 10.1016/j.fss.2025.109698

Matjaž Omladič , Martin Vuk , Aljaž Zalar

引用次数: 0

Computational complexity of some MaxSAT problems in Łukasiewicz logic Łukasiewicz逻辑中一些MaxSAT问题的计算复杂度

IF 2.7 1区数学

Fuzzy Sets and Systems Pub Date : 2026-03-15 Epub Date: 2025-11-19 DOI: 10.1016/j.fss.2025.109664

Serafina Lapenta , Sebastiano Napolitano

引用次数: 0

Fully & partially-transmitted-rule fusion: A novel hierarchical fuzzy classification with application to nasopharyngeal cancer’s metastasis prediction 全和部分传递规则融合：一种新的层次模糊分类方法在鼻咽癌转移预测中的应用

IF 2.7 1区数学

Fuzzy Sets and Systems Pub Date : 2026-03-15 Epub Date: 2025-11-20 DOI: 10.1016/j.fss.2025.109695

Ta Zhou , Yuanqing Yang , Wei Yan , Weiqin Liu , Xibei Yang , Weiping Ding , Jing Cai , Shitong Wang

{"title":"Fully & partially-transmitted-rule fusion: A novel hierarchical fuzzy classification with application to nasopharyngeal cancer’s metastasis prediction","authors":"Ta Zhou , Yuanqing Yang , Wei Yan , Weiqin Liu , Xibei Yang , Weiping Ding , Jing Cai , Shitong Wang","doi":"10.1016/j.fss.2025.109695","DOIUrl":"10.1016/j.fss.2025.109695","url":null,"abstract":"<div><div>Distant metastasis (DM) as a major cause of treatment failure of nasopharyngeal cancer (NPC) actually occurs with a considerably gradual development in the early stage. Therefore, an ideal DM prediction model should be an efficient and interpretable model and simultaneously reflect/simulate this characteristic during its training. Towards such a goal, this study proposes a hierarchical Takagi-Sugeno-Kang (TSK) fuzzy classifier (H-TSKFC) to assure both enhanced classification performance and diversified generation of interpretable fuzzy rules therein through full-partial-rule-transmission fusion for simulating gradual development of DM. Profiting from full-partial-rule-transmission fusion between sub-classifiers, H-TSKFC was endowed with the following benefits. Firstly, a novel stacking mechanism without any use of residuals between sub-classifiers enhances its generalization capability. Secondly, the generation of interpretable fuzzy rules from the second TSK fuzzy sub-classifier provides a diversified way. That is, its useful rules fusion transmitted fully or partially from previous sub-classifier guarantees considerable consistency between sub-classifiers, while its remaining rules reflect gradual difference between them. In this way, the H-TSKFC’s structure naturally mimics the gradual development of DM. Finally, each sub-classifier therein can be trained sequentially and quickly with an analytical solution to accomplish an individual prediction on the original inputs and outputs. Experimental results indeed demonstrate that H-TSKFC possesses linguistic interpretability, along with considerable classification and generalization performance.</div></div>","PeriodicalId":55130,"journal":{"name":"Fuzzy Sets and Systems","volume":"527 ","pages":"Article 109695"},"PeriodicalIF":2.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel approach to rough set model via neighborhood systems 基于邻域系统的粗糙集模型的一种新方法

IF 2.7 1区数学

Fuzzy Sets and Systems Pub Date : 2026-03-15 Epub Date: 2025-11-23 DOI: 10.1016/j.fss.2025.109694

Xinyu Shi , Liwen Ma

引用次数: 0

Tight spans of fuzzy metric spaces 模糊度量空间的紧跨度

IF 2.7 1区数学

Fuzzy Sets and Systems Pub Date : 2026-03-15 Epub Date: 2025-11-22 DOI: 10.1016/j.fss.2025.109700

Yi Shi , Hui Yang

引用次数: 0

Type-1 fuzzy functions for forecasting: A literature review and bibliometric analysis 一类模糊函数预测：文献回顾与计量分析

IF 2.7 1区数学

Fuzzy Sets and Systems Pub Date : 2026-03-15 Epub Date: 2025-11-27 DOI: 10.1016/j.fss.2025.109697

Aysegul Yabaci Tak , Nihat Tak

{"title":"Type-1 fuzzy functions for forecasting: A literature review and bibliometric analysis","authors":"Aysegul Yabaci Tak , Nihat Tak","doi":"10.1016/j.fss.2025.109697","DOIUrl":"10.1016/j.fss.2025.109697","url":null,"abstract":"<div><div>Type-1 Fuzzy Functions (T1FFs) have emerged as a rule-free alternative to classical fuzzy inference systems for tackling forecasting problems, especially in the presence of uncertainty and nonlinearity. This study provides a comprehensive literature review and a bibliometric analysis of T1FFs in the context of forecasting. A dataset of 25 articles indexed in the Web of Science Core Collection was examined to assess research trends, author collaboration networks, influential publications, source journals, and key thematic areas. Our review identifies four main components in the design of T1FF-based models: input structure, clustering methods, forecasting models, and objective function optimization. Through citation and co-authorship network analysis, we highlight prominent researchers and collaborations within the field. Source journal analysis reveals publication hotspots, while co-word analysis identifies dominant themes such as “forecasting,” “robust regression,” and “metaheuristic optimization.” The results suggest growing academic interest and methodological diversification in the use of T1FFs, with Turkey leading international contributions. This study serves as a roadmap for researchers aiming to build or extend T1FF-based forecasting systems.</div></div>","PeriodicalId":55130,"journal":{"name":"Fuzzy Sets and Systems","volume":"527 ","pages":"Article 109697"},"PeriodicalIF":2.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0