IEEE Transactions on Fuzzy Systems最新文献_第10页

Adaptive Fuzzy Safety Control of Hypersonic Flight Vehicles Pursuing Adaptable Prescribed Behaviors: A Sensing and Adjustment Mechanism 高超音速飞行器追求适应性规定行为的自适应模糊安全控制：感知和调整机制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-10-07 DOI: 10.1109/TFUZZ.2024.3476393

Xiangwei Bu;Ruining Luo;Maolong Lv;Humin Lei

{"title":"Adaptive Fuzzy Safety Control of Hypersonic Flight Vehicles Pursuing Adaptable Prescribed Behaviors: A Sensing and Adjustment Mechanism","authors":"Xiangwei Bu;Ruining Luo;Maolong Lv;Humin Lei","doi":"10.1109/TFUZZ.2024.3476393","DOIUrl":"10.1109/TFUZZ.2024.3476393","url":null,"abstract":"The perturbations in model parameters of hypersonic flight vehicles (HFVs) are highly likely to induce fluctuations in control error, which can potentially render the existing prescribed performance control (PPC) singular and pose a threat to flight safety. Therefore, our objective is to propose an adaptive fuzzy safety control protocol for HFVs that aims to achieve adaptable prescribed behaviors in the presence of parameter perturbations. To accomplish this, we initially develop a novel error-sensing system for timely detection and forecasting of error fluctuations. Building upon this foundation, we further define an adjustment mechanism that appropriately adjusts the upper envelope upward and the lower envelope downward at regular intervals. In contrast to existing fixed PPC approaches, the proposed sensing and adjustment mechanism enables both velocity and altitude tracking errors to satisfy a new type of adaptable prescribed qualities, thereby ensuring safe flight control of HFVs. In addition, we explore low-computational-burden fuzzy approximation techniques that minimize the required online adaptive parameters while guaranteeing excellent real-time control performance. Finally, comparative simulations are conducted to validate the proposed method.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"32 12","pages":"7050-7062"},"PeriodicalIF":10.7,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142384415","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

IEEE Computational Intelligence Society Information 电气和电子工程师学会计算智能学会信息

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-10-07 DOI: 10.1109/TFUZZ.2024.3463849

引用次数: 0

A Reconstructed UNet Model With Hybrid Fuzzy Pooling for Gastric Cancer Segmentation in Tissue Pathology Images 采用混合模糊池法重建 UNet 模型，用于组织病理学图像中的胃癌分段

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-10-04 DOI: 10.1109/tfuzz.2024.3474699

Junjun Huang, Shier Nee Saw, Yanlin Chen, Dongdong Hu, Xufeng Sun, Ning Chen, Loo Chu Kiong

引用次数: 0

Fuzzy Rule-Based Test-Time Adaptation for Class Imbalance in Dynamic Scenarios 基于模糊规则的测试时间调整，解决动态场景中的类别不平衡问题

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-10-03 DOI: 10.1109/tfuzz.2024.3459914

Ran Wang, Hua Zuo, Jie Lu

引用次数: 0

FRCNN: A Combination of Fuzzy-rough-set-based Feature Discretization and Convolutional Neural Network for Segmenting Subretinal Fluid Lesions FRCNN：基于模糊粗糙集的特征离散化与卷积神经网络的结合，用于分割视网膜下积液病变

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-10-03 DOI: 10.1109/tfuzz.2024.3473310

Qiong Chen, Lirong Zeng, Weiping Ding

引用次数: 0

Averaging Functions on Triangular Fuzzy Numbers and an Application in Graphs 三角模糊数的平均函数及其在图形中的应用

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-10-03 DOI: 10.1109/TFUZZ.2024.3473791

Nicolás Zumelzu;Roberto Díaz;Aldryn Aparcana;José Canumán;Álvaro Mella;Edmundo Mansilla;Diego Soto;Benjamín Bedregal

引用次数: 0

Adversarial Attack Detection via Fuzzy Predictions 通过模糊预测检测对抗性攻击

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-10-03 DOI: 10.1109/TFUZZ.2024.3473768

Yi Li;Plamen Angelov;Neeraj Suri

{"title":"Adversarial Attack Detection via Fuzzy Predictions","authors":"Yi Li;Plamen Angelov;Neeraj Suri","doi":"10.1109/TFUZZ.2024.3473768","DOIUrl":"10.1109/TFUZZ.2024.3473768","url":null,"abstract":"Image processing using neural networks act as a tool to speed up predictions for users, specifically on large-scale image samples. To guarantee the clean data for training accuracy, various deep learning-based adversarial attack detection techniques have been proposed. These crisp set-based detection methods directly determine whether an image is clean or attacked, while, calculating the loss is nondifferentiable and hinders training through normal back-propagation. Motivated by the recent success in fuzzy systems, in this work, we present an attack detection method to further improve detection performance, which is suitable for any pretrained neural network classifier. Subsequently, the fuzzification network is used to obtain feature maps to produce fuzzy sets of difference degree between clean and attacked images. The fuzzy rules control the intelligence that determines the detection boundaries. Different from previous fuzzy systems, we propose a fuzzy mean-intelligence mechanism with new support and confidence functions to improve fuzzy rule's quality. In the defuzzification layer, the fuzzy prediction from the intelligence is mapped back into the crisp model predictions for images. The loss between the prediction and label controls the rules to train the fuzzy detector. We show that the fuzzy rule-based network learns rich feature information than binary outputs and offer to obtain an overall performance gain. Experiment results show that compared to various benchmark fuzzy systems and adversarial attack detection methods, our fuzzy detector achieves better detection performance over a wide range of images.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"32 12","pages":"7015-7024"},"PeriodicalIF":10.7,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374189","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

Design Trend Fuzzy Granulation-Based Three-Layer Fuzzy Cognitive Map for Long-Term Forecasting of Multivariate Time Series 基于三层模糊认知图的多变量时间序列长期预测的模糊粒化设计趋势

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-10-03 DOI: 10.1109/TFUZZ.2024.3474476

Fei Yang;Fusheng Yu;Chenxi Ouyang;Yuqing Tang

{"title":"Design Trend Fuzzy Granulation-Based Three-Layer Fuzzy Cognitive Map for Long-Term Forecasting of Multivariate Time Series","authors":"Fei Yang;Fusheng Yu;Chenxi Ouyang;Yuqing Tang","doi":"10.1109/TFUZZ.2024.3474476","DOIUrl":"10.1109/TFUZZ.2024.3474476","url":null,"abstract":"Fuzzy cognitive maps (FCMs) are directed graphs with multiple nodes, rendering them well-suited for tackling the challenges of multivariate time series (MTS) forecasting. However, the conventional FCMs encounter obstacles in long-term forecasting, primarily due to the cumulated errors arising from iterative one-step forecasting. Drawing inspiration from recent advancements on fuzzy information granulation, this article introduces a novel trend fuzzy granulation-based three-layer FCM model that operates at a granular level, effectively addressing abovementioned obstacles. This model leverages an optimization algorithm to determine the optimal number of granules for granulating an MTS into a granular time series (GTS), enabling the simultaneous consideration of trend information across various dimensions of the given MTS. Subsequently, viewing the obtained GTS as a complex structured MTS, a novel three-layer FCM architecture is devised. This FCM comprises a layer-3 FCM for extracting spatial relationships among parameters, a layer-2 FCM for extracting spatial relationships among variables, and a layer-1 FCM for capturing temporal relationships. By embedding the layer-3 FCM into the nodes of the layer-2 FCM and further embedding the layer-2 FCM into the nodes of the layer-1 FCM, the three-layer FCM can effectively capture and reflect temporal and spatial relationships while treating each complex element of the obtained GTS as a cohesive entity during forecasting. By constructing the three-layer FCM-based model at a granular level for MTS, the proposed approach mitigates accumulated errors and enhance the ability to forecast future trends with superior accuracy.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"32 12","pages":"7037-7049"},"PeriodicalIF":10.7,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374191","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

Weakly Supervised Causal Discovery Based on Fuzzy Knowledge and Complex Data Complementarity 基于模糊知识和复杂数据互补性的弱监督因果发现

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-10-03 DOI: 10.1109/TFUZZ.2024.3471187

Wenrui Li;Wei Zhang;Qinghao Zhang;Xuegong Zhang;Xiaowo Wang

{"title":"Weakly Supervised Causal Discovery Based on Fuzzy Knowledge and Complex Data Complementarity","authors":"Wenrui Li;Wei Zhang;Qinghao Zhang;Xuegong Zhang;Xiaowo Wang","doi":"10.1109/TFUZZ.2024.3471187","DOIUrl":"10.1109/TFUZZ.2024.3471187","url":null,"abstract":"Causal discovery based on observational data is important for deciphering the causal mechanism behind complex systems. However, the effectiveness of existing causal discovery methods is limited due to inferior prior knowledge, domain inconsistencies, and the challenges of high-dimensional datasets with small sample sizes. To address this gap, we propose a novel weakly supervised fuzzy knowledge and data co-driven causal discovery method named KEEL. KEEL introduces a fuzzy causal knowledge schema to encapsulate diverse types of fuzzy knowledge, and forms corresponding weakened constraints. This schema not only lessens the dependency on expertise but also allows various types of limited and error-prone fuzzy knowledge to guide causal discovery. It can enhance the generalization and robustness of causal discovery, especially in high-dimensional and small-sample scenarios. In addition, we integrate the extended linear causal model into KEEL for dealing with the multi-distribution and incomplete data. Extensive experiments with different datasets demonstrate the superiority of KEEL over several state-of-the-art methods in accuracy, robustness and efficiency. The effectiveness of KEEL is also verified in limited real protein signal transduction process data, with the better performance than benchmark methods. In summary, KEEL is effective to tackle the causal discovery tasks with higher accuracy while alleviating the requirement for extensive domain expertise.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"32 12","pages":"7002-7014"},"PeriodicalIF":10.7,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374188","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 Hybrid Model Integrating Fuzzy Systems and Convolutional Factorization Machine for Delivery Time Prediction in Intelligent Logistics 用于智能物流中交货时间预测的模糊系统与卷积因果化机混合模型

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2024-10-01 DOI: 10.1109/tfuzz.2024.3472043

Delong Zhu, Zhong Han, Xing Du, Dafa Zuo, Liang Cai, Changchun Xue

引用次数: 0