IEEE Transactions on Systems Man Cybernetics-Systems最新文献_第3页

Global Asymptotic Stability for Delayed Neural Networks via High-Degree Reciprocally Convex Inequality 基于高次互凸不等式的延迟神经网络的全局渐近稳定性

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-02-23 DOI: 10.1109/TSMC.2026.3663453

Hao Ying;Sanyang Liu;Kaibo Shi;Shouming Zhong;Kun Zhou

引用次数: 0

Weighted Fusion of Classifiers With Approximate Reasoning and Reliability Evaluation for Multisource Information Fusion 基于近似推理的分类器加权融合及多源信息融合的可靠性评估

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-03-02 DOI: 10.1109/TSMC.2026.3665986

Kezhu Zuo;Xinde Li;Le Yu;Huaping Liu;Yilin Dong;Jean Dezert;Tao Shen;Shuzhi Sam Ge

{"title":"Weighted Fusion of Classifiers With Approximate Reasoning and Reliability Evaluation for Multisource Information Fusion","authors":"Kezhu Zuo;Xinde Li;Le Yu;Huaping Liu;Yilin Dong;Jean Dezert;Tao Shen;Shuzhi Sam Ge","doi":"10.1109/TSMC.2026.3665986","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3665986","url":null,"abstract":"Classifiers fusion can be seen as a kind of multisource information fusion (MSIF), and classifiers fusion based on Dempster–Shafer (DS) evidence theory is an effective approach to improve the accuracy of classification tasks. However, different classifiers usually exhibit varying performances, making it challenging to achieve enhanced classification accuracy through direct fusion. Simultaneously, when the frame of discernment (FoD) of the target class expands, the number of focal elements involved in the fusion increases, resulting in a rapid growth in computational complexity. To enhance the classification performance while reducing the time cost of fusion, a novel weighted fusion of classifiers method based on approximate reasoning and reliability evaluation (WFC-AR-RE) is proposed in this article. Specifically, at first, the key focal elements are determined based on the outputs of classifiers, and an approximate basic belief assignment (BBA) is generated. Subsequently, the validation set is utilized to evaluate the performance of each classifier, thus obtaining the self-reliability of each BBA. Afterward, a novel divergence measure is introduced to quantify the discrepancy between BBAs, determining the relative reliability of each BBA. Finally, the fusion weight of each BBA is derived from its self-reliability and relative reliability, and Dempster’s rule is applied to combine the weighted BBA. The proposed WFC-AR-RE algorithm is applied to the MSIF system, and its effectiveness is demonstrated on 12 public datasets.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 7","pages":"4269-4283"},"PeriodicalIF":8.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442722","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

Tensorized Fine-Grained Incomplete Multiview Clustering via Intrinsic Structure Recovery 基于内在结构恢复的张紧化细粒度不完全多视图聚类

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-04-01 DOI: 10.1109/TSMC.2026.3678289

Huibing Wang;Luyan Cui;Mingze Yao;Xianping Fu

{"title":"Tensorized Fine-Grained Incomplete Multiview Clustering via Intrinsic Structure Recovery","authors":"Huibing Wang;Luyan Cui;Mingze Yao;Xianping Fu","doi":"10.1109/TSMC.2026.3678289","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3678289","url":null,"abstract":"Incomplete multiview clustering (IMVC) focuses on exploiting the complementary and consistent information from multiple incomplete views for dividing unlabeled multiview data into corresponding clusters. Most existing methods seek to recover the missing samples of the views while inevitably having an influence on the intrinsic structure of the original space. Moreover, previous IMVC algorithms treat the samples of each view equally, which learns the consensus representation in a view-level manner and thus neglects that different views contribute to each individual sample diversely. These situations are a limitation to effective recovery of absent samples and fuse the heterogeneous information, which leads to suboptimal clustering performance. To tackle the above issues, this article proposes a novel approach called tensorized fine-grained IMVC via intrinsic structure recovery (TFIR), which flexibly recovers the intrinsic structure of the original data and attains the unified representation based on the fine-grained fusion strategy. Specifically, TFIR infers the incomplete data via available instances’ relations to improve the accuracy of learned intersample-specific representations. Afterward, TFIR stacks the specific representations of multiple views into a tensor to preserve the intrasample consistency. Finally, TFIR explores the complementarity among various views from the fine-grained sample perspective to obtain a rich underlying structure for clustering. Experimental results on the eight benchmark datasets clearly verify the remarkable superiority of TFIR compared with the most state-of-the-art methods.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 7","pages":"4370-4383"},"PeriodicalIF":8.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442963","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

Together, we are advancing technology 我们共同推动技术进步

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-06-19 DOI: 10.1109/TSMC.2026.3700362

引用次数: 0

Predefined-Time and Predefined-Accuracy Adaptive Neural Tracking Control of Disturbed Nonstrict-Feedback Nonlinear Systems Under Asymmetric Output Constraints 非对称输出约束下扰动非严格反馈非线性系统的预定义时间和预定义精度自适应神经跟踪控制

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-02-18 DOI: 10.1109/TSMC.2026.3662749

Yu Zhang;Ben Niu;Xudong Zhao;Shuping Ma;Huanqing Wang

{"title":"Predefined-Time and Predefined-Accuracy Adaptive Neural Tracking Control of Disturbed Nonstrict-Feedback Nonlinear Systems Under Asymmetric Output Constraints","authors":"Yu Zhang;Ben Niu;Xudong Zhao;Shuping Ma;Huanqing Wang","doi":"10.1109/TSMC.2026.3662749","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3662749","url":null,"abstract":"This work concentrates on the predefined-time and predefined-accuracy tracking controller design problem for a nonstrict-feedback nonlinear system under asymmetric output constraints. Unlike finite-time or fixed-time control strategies in related literature, the great advantage of our presented controller design algorithm is that the algorithm can specify the convergence time and the accuracy of the system in advance, depending on the user’s requirements. First, an extremely important performance of RBF NNs is employed to handle the design obstacle caused by nonstrict-feedback structure. Then, a time-varying nonlinear transformation function is introduced to handle the asymmetric output constraints problems. Second, under the framework of adaptive backstepping design, we propose the predefined-time controller and the asymptotic tracking controller simultaneously, and a continuous switching function is used to transition the predefined-time controller to the asymptotic tracking controller when the resulting tracking error arrives at the specified accuracy. Under our control scheme, it is rigorously demonstrated that all signals of the studied system remain bounded and the tracking error achieves a predefined accuracy within a preset time. Finally, two practical simulation experiments are given to validate the effectiveness of the controller.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 7","pages":"4086-4096"},"PeriodicalIF":8.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148505774","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

Efficient Self-Supervised Grading of Prostate Cancer Pathology 前列腺癌病理的有效自我监督分级

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-02-18 DOI: 10.1109/TSMC.2026.3662748

Riddhasree Bhattacharyya;Surochita Pal;Sushmita Mitra

{"title":"Efficient Self-Supervised Grading of Prostate Cancer Pathology","authors":"Riddhasree Bhattacharyya;Surochita Pal;Sushmita Mitra","doi":"10.1109/TSMC.2026.3662748","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3662748","url":null,"abstract":"Prostate cancer grading, using the International Society of Urological Pathology (ISUP) system, for treatment decisions is highly subjective and requires considerable expertise. Despite advances in computer-aided diagnosis systems, few have handled efficient ISUP grading on whole slide images (WSIs) of prostate biopsies based only on slide labels. In this scenario, TSOR is developed, where a novel task-specific self-supervised learning (SSL) framework is used for patch-level pretraining. This is fine-tuned using ordinal regression for WSI-level ISUP grading. One of the main challenges faced by deep learning (DL) in ISUP grading, is the learning of patch-level features based on slide labels. Though using models pretrained at patch-level using SSL or other paradigms is the most obvious choice here, the diversity of training samples plays a crucial role in effective pretraining. However, pretraining on a large database of different histopathology images becomes computationally expensive. Therefore, a patch-level dataset (relatively balanced with respect to the patch-level grades) is initially created for effective SSL-based pretraining. As stain-variation across centers leads to difficulty in generalization, an additional loss term is incorporated to effectively learn the stain-agnostic patch-level features. As it is desirable that misclassification be as close as possible to the actual grade, in medical images, we fine-tune the pretrained network for WSI-level ISUP grading using an ordinal regression-based approach. Experimental results on the most extensive prostate cancer grade assessment (PANDA) challenge dataset, and the SICAPv2 dataset, demonstrate the effectiveness of TSOR compared to state-of-the-art (SOTA) methods.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 7","pages":"4113-4125"},"PeriodicalIF":8.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148505968","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

Prescribed-Time Tracking Control for Nonlinear Systems: A Nonvanishing Disturbance Observer-Based Approach 非线性系统的规定时间跟踪控制：一种基于非消失扰动观测器的方法

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-02-18 DOI: 10.1109/TSMC.2026.3659217

Yulong Ji;Xiucai Huang;Ben Niu;Yafeng Li;Xudong Zhao;Guangjing Song

{"title":"Prescribed-Time Tracking Control for Nonlinear Systems: A Nonvanishing Disturbance Observer-Based Approach","authors":"Yulong Ji;Xiucai Huang;Ben Niu;Yafeng Li;Xudong Zhao;Guangjing Song","doi":"10.1109/TSMC.2026.3659217","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3659217","url":null,"abstract":"This article investigates the problem of observer-based prescribed-time tracking control (OPTC) for a class of nonlinear systems with uncertain nonvanishing disturbances. First, a prescribed-time observer (PTO) with a well-designed time-varying high gain is constructed to achieve zero-error observation of the uncertain nonvanishing disturbances within the prescribed observation time. Subsequently, a two-stage controller design framework is proposed to construct the desired control input, which consists of two distinct components: the first-stage controller and the second-stage controller. The first-stage controller is established to compensate for the effects of the system’s nonlinear dynamics, thereby providing a relatively satisfactory tracking performance. The second-stage controller, on the other hand, is constructed based on a sophisticated switching mechanism of the control gains, which not only ensures that the tracking error converges to zero within the prescribed tracking time but also guarantees a smooth transition between the two-stage controllers. Finally, the simulation example involving a disturbed single-link flexible-joint robotic manipulator is presented to demonstrate the effectiveness of the proposed control strategy.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 7","pages":"4037-4046"},"PeriodicalIF":8.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506405","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 Transactions on Systems, Man, and Cybernetics: Systems Information for Authors IEEE系统、人与控制论汇刊：作者的系统信息

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-06-19 DOI: 10.1109/TSMC.2026.3700370

引用次数: 0

State Estimation of Markov Jump Neural Networks With Sensor Resolution and Innovation Saturation: A Binary-Encoding Scheme 具有传感器分辨率和创新饱和度的马尔可夫跳神经网络状态估计：一种二进制编码方案

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-03-26 DOI: 10.1109/TSMC.2026.3675037

Xiaobin Gao;Feiqi Deng;Xueyan Zhao;Pengyu Zeng;Lixue Wang

引用次数: 0

CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation---A Deep Learning Framework for Smart Manufacturing 用于工业表示和评估的压缩潜在自动编码器——智能制造的深度学习框架

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-07-01 Epub Date: 2026-03-19 DOI: 10.1109/TSMC.2026.3671163

Mohammadhossein Ghahramani;MengChu Zhou

{"title":"CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation---A Deep Learning Framework for Smart Manufacturing","authors":"Mohammadhossein Ghahramani;MengChu Zhou","doi":"10.1109/TSMC.2026.3671163","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3671163","url":null,"abstract":"Accurate fault detection in high-dimensional industrial environments remains a major challenge due to the inherent complexity, noise, and redundancy in sensor data. This article introduces compressed latent autoencoder for industrial representation and evaluation (CLAIRE), that is, a hybrid end-to-end learning framework that integrates unsupervised deep representation learning with supervised classification for intelligent quality control in smart manufacturing systems. It employs an optimized deep autoencoder to transform raw input into a compact latent space, effectively capturing the intrinsic data structure while suppressing irrelevant or noisy features. The learned representations are then fed into a downstream classifier to perform binary fault prediction. Experimental results on a high-dimensional dataset demonstrate that CLAIRE significantly outperforms conventional classifiers trained directly on raw features. Moreover, it incorporates a post hoc phase, using a game-theory-based interpretability technique, to analyze the latent space and identify the most informative input features contributing to fault predictions. The proposed framework highlights the potential of integrating explainable artificial intelligence with feature-aware regularization for robust fault detection. The modular and interpretable nature of the proposed framework makes it highly adaptable, offering promising applications in other domains characterized by complex, high-dimensional data, e.g., healthcare, finance, and environmental monitoring.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 7","pages":"4229-4241"},"PeriodicalIF":8.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442808","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