IEEE Transactions on Fuzzy Systems最新文献_第8页

A Fuzzy Logic-Based Approach to Predict Human Interaction by Functional Near-Infrared Spectroscopy 基于模糊逻辑的功能近红外光谱预测人类互动的方法

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-22 DOI: 10.1109/TFUZZ.2025.3528376

Xiaowei Jiang;Liang Ou;Yanan Chen;Na Ao;Yu-Cheng Chang;Thomas Do;Chin-Teng Lin

{"title":"A Fuzzy Logic-Based Approach to Predict Human Interaction by Functional Near-Infrared Spectroscopy","authors":"Xiaowei Jiang;Liang Ou;Yanan Chen;Na Ao;Yu-Cheng Chang;Thomas Do;Chin-Teng Lin","doi":"10.1109/TFUZZ.2025.3528376","DOIUrl":"10.1109/TFUZZ.2025.3528376","url":null,"abstract":"In this article, we introduce the Fuzzy logic-based attention (Fuzzy Attention Layer) mechanism, a novel computational approach designed to enhance the interpretability and efficacy of neural models in psychological research. The fuzzy attention layer integrated into the transformer encoder model to analyze complex psychological phenomena from neural signals captured by functional near-infrared spectroscopy (fNIRS). By leveraging fuzzy logic, the fuzzy attention layer learns and identifies interpretable patterns of neural activity. This addresses a significant challenge in using transformers: the lack of transparency in determining which specific brain activities most contribute to particular predictions. Our experimental results, obtained from fNIRS data engaged in social interactions involving handholding, reveal that the fuzzy attention layer not only learns interpretable patterns of neural activity but also enhances model performance. In addition, these patterns provide deeper insights into the neural correlates of interpersonal touch and emotional exchange. The application of our model shows promising potential in understanding the complex aspects of human social behavior, verify psychological theory with machine learning algorithms, thereby contributing significantly to the fields of social neuroscience and AI.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 5","pages":"1664-1677"},"PeriodicalIF":10.7,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online Self-Learning Fuzzy Recurrent Stochastic Configuration Networks for Modeling Nonstationary Dynamics 用于非平稳动力学建模的在线自学习模糊递归随机组态网络

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-22 DOI: 10.1109/tfuzz.2025.3532652

Gang Dang, Dianhui Wang

引用次数: 0

Guaranteed State Estimation for $mathscr {H}_-/mathscr {L}_infty$ Fault Detection of Uncertain Takagi-Sugeno Fuzzy Systems With Unmeasured Nonlinear Consequents 具有不可测非线性后果的不确定Takagi-Sugeno模糊系统$mathscr {H}_-/mathscr {L}_infty$故障检测的保证状态估计

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-21 DOI: 10.1109/tfuzz.2025.3532221

Masoud Pourasghar, Anh-Tu Nguyen, Thierry-Marie Guerra

引用次数: 0

A Fuzzy Measure and Choquet Integral-Based Approach in the Predictive Knowledge-Based Systems: Application to the Electricity Demand Forecasting 基于模糊测度和Choquet积分的预测知识系统方法在电力需求预测中的应用

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-17 DOI: 10.1109/TFUZZ.2024.3525081

M. Rostam Niakan Kalhori;Gleb Beliakov

{"title":"A Fuzzy Measure and Choquet Integral-Based Approach in the Predictive Knowledge-Based Systems: Application to the Electricity Demand Forecasting","authors":"M. Rostam Niakan Kalhori;Gleb Beliakov","doi":"10.1109/TFUZZ.2024.3525081","DOIUrl":"10.1109/TFUZZ.2024.3525081","url":null,"abstract":"In a data-driven knowledge-based forecasting system, knowledge can be extracted from historical data by machine learning methods, and represented in the knowledge base. In the inference engine of such a system, the current inputs are used to forecast the likely outputs. The unpredictability of one (or more) inputs in the forecasting horizon, gives rise to one source of uncertainty in the reasoning process, named evidence uncertainty, which is the main focus of this article. To deal with this sort of uncertainty numerical scenarios are generated from historical data to substitute the uncertain input parameter using two approaches: Uniform sampling, and a classical nonuniform sampling acceptance–rejection method. Each input scenario has particular output, and at the end these outputs are aggregated with the discrete Choquet integral to account for outputs' dependencies. Fuzzy measures, also called capacities or nonadditive measures, assign the importance weight not to just each scenario but also to groups of scenarios. A fuzzy measure for the Choquet integral is learned from the historical data. The proposed reasoning approach is evaluated in a long-term relative electricity load forecasting, and treats the uncertainty which arises from unpredictability of the daily temperature in the long run. The results show the superiority of the proposed Choquet integral-based approach with respect to the <inline-formula><tex-math>$k$</tex-math></inline-formula>-interactive, <inline-formula><tex-math>$k$</tex-math></inline-formula>-additive, and <inline-formula><tex-math>$k$</tex-math></inline-formula>-tolerant fuzzy measures compared to traditional aggregators: the weighted arithmetic mean, and the ordered weighted averaging.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 4","pages":"1379-1390"},"PeriodicalIF":10.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","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 Reliable Three-Way Multi-Classification Model Under Intuitionistic Fuzzy Environment 直觉模糊环境下一种新颖可靠的三向多分类模型

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-16 DOI: 10.1109/tfuzz.2025.3530773

Libo Zhang, Cong Guo, Tianxing Wang, Dun Liu, Huaxiong Li

引用次数: 0

Adaptive Incremental Broad Learning System Based on Interval Type-2 Fuzzy Set with Automatic Determination of Hyperparameters 基于区间2型模糊集超参数自动确定的自适应增量广义学习系统

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-16 DOI: 10.1109/tfuzz.2025.3530458

Haijie Wu, Weiwei Lin, Yuehong Chen, Fang Shi, Wangbo Shen, C. L. Philip Chen

引用次数: 0

Brain-Inspired Fuzzy Graph Convolution Network for Alzheimer's Disease Diagnosis Based on Imaging Genetics Data 基于影像遗传学数据的脑启发模糊图卷积网络阿尔茨海默病诊断

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-15 DOI: 10.1109/tfuzz.2025.3529304

Xia-An Bi, Yangjun Huang, Wenzhuo Shen, Zicheng Yang, Yuhua Mao, Luyun Xu, Zhonghua Liu

引用次数: 0

Adaptive Critic-Based Optimal Control of Input-Constrained Stochastic Systems via Generalized Fuzzy Hyperbolic Models 基于广义模糊双曲模型的输入约束随机系统自适应临界最优控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-15 DOI: 10.1109/TFUZZ.2024.3523898

Lulu Zhang;Huaguang Zhang;Jiayue Sun;Zhongyang Ming

引用次数: 0

Exponential Stability of Fractional-Order Fuzzy Multilayer Networks With Short Memory and Noninstantaneous Impulses via Intermittent Control 通过间歇控制实现短记忆和非瞬时脉冲的分数阶模糊多层网络的指数稳定性

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-15 DOI: 10.1109/TFUZZ.2025.3528975

Yao Xu;Zui Chen;Wenxue Li;Yongbao Wu

引用次数: 0

Granular Sequential Three-Way Decision for Specific Decision Classes 特定决策类别的粒度顺序三向决策

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-01-15 DOI: 10.1109/TFUZZ.2025.3529459

Yunlong Cheng;Xiuhua Yang;Qinghua Zhang;Yabin Shao;Guoyin Wang

{"title":"Granular Sequential Three-Way Decision for Specific Decision Classes","authors":"Yunlong Cheng;Xiuhua Yang;Qinghua Zhang;Yabin Shao;Guoyin Wang","doi":"10.1109/TFUZZ.2025.3529459","DOIUrl":"10.1109/TFUZZ.2025.3529459","url":null,"abstract":"Sequential three-way decision (S3WD) is an efficient granular computing paradigm for dealing with uncertain problems. However, it is primarily oriented to all decision classes, which contradicts the fact that decisions are typically for the specific decision classes. Meanwhile, most S3WD models hide the topological structure of the granules, leading to difficulties in semantic interpretation. To address the issues, integrating model construction, attribute reduction and knowledge extraction, a general framework of granular sequential three-way decision for the specific decision classes is proposed to improve semantic interpretation and computational efficiency. First, a two-stage trisecting strategy and a GrS3WD model are proposed to integrate model construction with attribute reduction. Its main advantage is that it retains the topological structure of granules, which not only enhances semantic interpretation, but also avoids unnecessary double counting. Second, three acceleration strategies and a novel granular sequential three-way reduction (GrS3WR) algorithm are proposed to fast obtain a classification-based reduct or a class-specific reduct. Finally, the decision rules with multigranularity can be directly extracted from the concept tree generated by GrS3WR. Experimental results demonstrate that a class-specific reduct usually has fewer attributes and better classification performance than a classification-based reduct. Moreover, GrS3WR can significantly improve the computational efficiency of attribute reduction.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 5","pages":"1650-1663"},"PeriodicalIF":10.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0