Neurocomputing最新文献_第3页

XID: A protocol for evaluating identity consistency under domain shifts and reidentification method XID：一种域移位下的恒等一致性评估协议和再识别方法

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-14 DOI: 10.1016/j.neucom.2026.133012

Seongyeop Yang , Minho Kim , Byeongkeun Kang , Yeejin Lee

{"title":"XID: A protocol for evaluating identity consistency under domain shifts and reidentification method","authors":"Seongyeop Yang , Minho Kim , Byeongkeun Kang , Yeejin Lee","doi":"10.1016/j.neucom.2026.133012","DOIUrl":"10.1016/j.neucom.2026.133012","url":null,"abstract":"<div><div>Recent domain generalization methods for person reidentification aim to learn features that remain discriminative across domains to improve performance in unseen environments. Prior work has addressed domain shift through discrepancy reduction and alternative normalization strategies, while maintaining identity separability. However, these evaluations often rely on simplified settings with non-overlapping identities and limited visual diversity. To address this, we propose a new evaluation protocol that introduces identity transfer and significant appearance variation by constructing query and gallery sets from different domains. This setup enables a more realistic assessment of intra-class variation and inter-class discriminability. We further develop a learning framework specifically designed for this protocol, which enhances generalization by regulating achromatic information and projecting embeddings into a space that simulates unseen domains. The framework includes a self-regulating augmentation policy that adjusts transformation strength during training. Extensive experiments show consistent performance gains under both the proposed and standard protocols, establishing a more rigorous and practical benchmark.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"676 ","pages":"Article 133012"},"PeriodicalIF":6.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Data-driven adaptive secure collision-free formation tracking of networked marine surface vehicles under DoS attacks DoS攻击下网络水面舰艇数据驱动自适应安全无碰撞编队跟踪

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-14 DOI: 10.1016/j.neucom.2026.133082

Jun Hu , Jie Wu , Xisheng Zhan , Tao Han , Huaicheng Yan

{"title":"Data-driven adaptive secure collision-free formation tracking of networked marine surface vehicles under DoS attacks","authors":"Jun Hu , Jie Wu , Xisheng Zhan , Tao Han , Huaicheng Yan","doi":"10.1016/j.neucom.2026.133082","DOIUrl":"10.1016/j.neucom.2026.133082","url":null,"abstract":"<div><div>This paper addresses the adaptive path planning and secure formation tracking (SFT) control problem for networked marine surface vehicles (NMSVs) subject to denial-of-service (DoS) attacks, model uncertainty, external disturbances, and actuator faults. A hierarchical adaptive formation planning and control (HAFPC) framework is proposed. In its path planning layer, reinforcement learning (RL) based greedy path map inference (GPMI) infers local maps in unknown environments, while a Euclidean-distance-field-based formation path planning (EDF-FPP) algorithm finds collision-free trajectories. In the network layer, a distributed resilient estimator is designed to accurately estimate the virtual leader information under DoS attacks in directed graphs. In the control layer, a neural network (NN)-based data-driven observer is first employed to address model uncertainty. Then, an adaptive offset function and a data-driven observer-based control (DDOBC) algorithm are adopted to achieve SFT, obstacle avoidance, and handle input saturation and actuator faults. Lyapunov stability theory establishes sufficient conditions for system convergence and stabilization. Numerical simulations validate the proposed framework’s effectiveness.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"676 ","pages":"Article 133082"},"PeriodicalIF":6.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Self-organizing interval type-2 fuzzy neural networks with rule interaction constraints 具有规则交互约束的自组织区间型-2模糊神经网络

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-11 DOI: 10.1016/j.neucom.2026.133048

Yuanwen Zhang, Ke Zhang

{"title":"Self-organizing interval type-2 fuzzy neural networks with rule interaction constraints","authors":"Yuanwen Zhang, Ke Zhang","doi":"10.1016/j.neucom.2026.133048","DOIUrl":"10.1016/j.neucom.2026.133048","url":null,"abstract":"<div><div>Interval Type-2 fuzzy neural networks (IT2FNNs) provide a robust framework for managing uncertainty in classification tasks; however, conventional architectures rely on linear aggregation and fail to capture pairwise rule interactions. This paper proposes a self-organizing IT2FNN designed to explicitly parameterize these rule dependencies. The framework incorporates a rule interaction layer utilizing factorization machines to identify synergistic and competitive effects between IF-THEN rules. To ensure semantic consistency, a knowledge-driven constraint mechanism derived from 2-additive Choquet integral theory guides the interaction parameters. Additionally, a differentiable gating strategy enables autonomous rule base refinement through sparse regularization. Evaluations on benchmark datasets demonstrate that the proposed architecture achieves superior classification accuracy while maintaining a parsimonious rule base, averaging approximately 10 active rules. This study indicates that explicit rule interaction modeling improves functional approximation, while structural constraints and pruning maintain the model’s compactness and linguistic interpretability.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"676 ","pages":"Article 133048"},"PeriodicalIF":6.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Dynamic hypergraph structure learning for spatio-temporal time series forecasting 时空时间序列预测的动态超图结构学习

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-11 DOI: 10.1016/j.neucom.2026.133014

ZhuoLin Li , NingNing Cui , Huo Wu , Zhongyun Bao , Subin Huang

{"title":"Dynamic hypergraph structure learning for spatio-temporal time series forecasting","authors":"ZhuoLin Li , NingNing Cui , Huo Wu , Zhongyun Bao , Subin Huang","doi":"10.1016/j.neucom.2026.133014","DOIUrl":"10.1016/j.neucom.2026.133014","url":null,"abstract":"<div><div>Accurate spatiotemporal forecasting hinges on capturing the intricate and dynamic relationships within data. While hypergraph neural networks have shown promise for capturing high-order interactions, existing methods typically rely on external prior knowledge or learned static hypergraph structures, thereby limiting their ability to capture dynamic variations. Moreover, directly learning the full hypergraph incidence matrix suffers from parameter redundancy and overfitting. To mitigate these drawbacks, we propose a novel Dynamic Hypergraph and Graph Structure Inference Model (DHGSIM), which simultaneously models pairwise and high-order relationships without external prior knowledge. Specifically, for dynamic high-order associations, we construct hypergraph structures leveraging low-rank factorization to boost parameter efficiency and mitigate overfitting. A dynamic routing mechanism is further applied to enable the learned hypergraph structure to interact adaptively with the input data, thereby refining hyperedge representations. In pairwise association modeling, we propose a dynamic graph structure learning method that incorporates a key node identification mechanism to capture crucial interactions. Finally, we decouple temporal and spatial feature extraction to improve efficiency and optimize the entire framework end-to-end. Comprehensive experiments on five widely-used benchmark datasets show that our method attains superior performance. The source code is publicly available at <span><span>https://github.com/ZhuoLinLi-shu/DHGSIM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"676 ","pages":"Article 133014"},"PeriodicalIF":6.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Spiking neural P systems with brain-derived neurotrophic factor 脑源性神经营养因子刺激神经P系统

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-07 DOI: 10.1016/j.neucom.2026.132990

Yue Zhao, Zhenjiao Lin, Feng Qi

{"title":"Spiking neural P systems with brain-derived neurotrophic factor","authors":"Yue Zhao, Zhenjiao Lin, Feng Qi","doi":"10.1016/j.neucom.2026.132990","DOIUrl":"10.1016/j.neucom.2026.132990","url":null,"abstract":"<div><div>Spiking Neural P systems (SN P systems), as a member of the third generation artificial neural network models, have been widely studied in recent years due to their high scalability, high parallelism, and low energy consumption. However, the original SN P systems have limitations in nonlinear learning and dynamic plasticity. We introduced brain-derived neurotrophic factor (BDNF) into SN P systems for the first time, integrated BDNF and its signaling pathways, and enhanced spike control and synaptic plasticity through four innovative rules, thereby simulating the role of BDNF in neuronal adaptation. We demonstrate that the system requires only 25 neurons to perform universal calculations, which is less than the number of neurons used by existing SN P variants. Additionally, experimental evaluations on function approximation and image classification tasks confirm that the model achieves state-of-the-art performance. In the function fitting task, the system structure is further simplified through visual training and pruning strategies while maintaining efficient nonlinear computing power and high interpretability, fully demonstrating the operation process of the BDNF-SN P systems in actual tasks. In image classification, both comparative and ablation studies across MNIST and four MedMNIST datasets confirm the superiority of the 5B-BDNF-SN P family, with the 5B-Gram-SN P variant achieving up to 99.56% accuracy on MNIST and 97.87% AUC on PathMNIST. Furthermore, the model demonstrates high inference efficiency with fewer parameters, validating its effectiveness and adaptability in both general and medical image classification tasks.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"676 ","pages":"Article 132990"},"PeriodicalIF":6.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Dynamic enhanced binning framework 动态增强的分组框架

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-11 DOI: 10.1016/j.neucom.2026.133033

Wei Jiang , XiCheng Yang , YuXin Wang , LingYuLe Wang

引用次数: 0

Drift-aware variational autoencoder-based anomaly detection with two-level ensembling 基于漂移感知变分自编码器的两级集成异常检测

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-13 DOI: 10.1016/j.neucom.2026.133061

Jin Li , Kleanthis Malialis , Christos G. Panayiotou , Marios M. Polycarpou

引用次数: 0

Neural network for censored expectile regression based on data augmentation 基于数据增广的删检期望回归神经网络

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-14 DOI: 10.1016/j.neucom.2026.133086

Wei Cao , Shanshan Wang

{"title":"Neural network for censored expectile regression based on data augmentation","authors":"Wei Cao , Shanshan Wang","doi":"10.1016/j.neucom.2026.133086","DOIUrl":"10.1016/j.neucom.2026.133086","url":null,"abstract":"<div><div>Expectile regression neural network (ERNN) is powerful tool for capturing heterogeneity and complex nonlinear structures in data. However, most existing research has primarily focused on fully observed data, with reletive limited attention given to scenarios involving censored observations. In this paper, we propose a data-augmentation–based ERNN algorithm, termed DAERNN, for modeling heterogeneous censored data with complex relationships among variables. The proposed DAERNN is flexible and capable of exploring potential nonlinear effects of covariates on the conditional expectiles of the response under various types of censoring, thereby enhancing its applicability to practical censored data analysis. Moreover, DAERNN can be readily implemented via a data augmentation strategy combined with a standard gradient-based optimization algorithm, which directly yields estimates of the conditional expectile functions. The advantages of the DAERNN are illustrated through extensive Monte Carlo simulation studies and two real data applications. The results show that DAERNN outperforms existing censored ERNN methods as well as other competing approaches.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"676 ","pages":"Article 133086"},"PeriodicalIF":6.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Dynamic transformer architecture for continual learning of multimodal tasks 用于持续学习多模态任务的动态变压器架构

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-07 DOI: 10.1016/j.neucom.2026.132977

Yuliang Cai , Mohammad Rostami

{"title":"Dynamic transformer architecture for continual learning of multimodal tasks","authors":"Yuliang Cai , Mohammad Rostami","doi":"10.1016/j.neucom.2026.132977","DOIUrl":"10.1016/j.neucom.2026.132977","url":null,"abstract":"<div><div>Transformer neural networks are increasingly replacing prior architectures across a wide range of applications in different data modalities. The increasing size and computational demands of fine-tuning large pre-trained transformer neural networks pose significant challenges for the widespread adoption of these models in applications that demand on-edge computing. To tackle this challenge, continual learning (CL) emerges as a solution by facilitating the transfer of knowledge across tasks that arrive sequentially for an autonomously learning agent. However, current CL methods mainly focus on learning tasks that are exclusively vision-based or language-based. We propose a transformer-based CL framework focusing on learning tasks that involve both vision and language, known as Vision-and-Language (VaL) tasks. In our framework, we benefit from the novel task-attention block and the introduced extra parameters to a base transformer to specialize the network for each task. As a result, we enable dynamic model expansion to learn several tasks in a sequence. We also use knowledge distillation and experience replay to benefit from relevant past experiences to learn the current task more efficiently. Our proposed method, Task Attentive Multimodal Continual Learning (TAM-CL), allows for the exchange of information between tasks while mitigating the problem of catastrophic forgetting. Notably, our approach is scalable, incurring minimal memory overhead. TAM-CL achieves 4.62% accuracy higher than the state-of-the-art (SOTA) accuracy on challenging multimodal tasks.<span><span><sup>2</sup></span></span></div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"676 ","pages":"Article 132977"},"PeriodicalIF":6.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Multi-view graph contrastive clustering via consensus constraint 基于共识约束的多视图图对比聚类

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2026-05-01 Epub Date: 2026-02-16 DOI: 10.1016/j.neucom.2026.133069

Wentao Li, Xingwang Zhao, Zhiqiang Li, Shiyi Li, Jinghan Yang

{"title":"Multi-view graph contrastive clustering via consensus constraint","authors":"Wentao Li, Xingwang Zhao, Zhiqiang Li, Shiyi Li, Jinghan Yang","doi":"10.1016/j.neucom.2026.133069","DOIUrl":"10.1016/j.neucom.2026.133069","url":null,"abstract":"<div><div>Multi-view graph clustering (MGC) aims to integrate graph information from multiple perspectives to determine a unified node partitioning scheme, thereby accurately mining the potential association rules of data. In recent years, contrastive learning-based MGC methods have attracted extensive attention due to their excellent generalization ability. However, existing mainstream edge-based strategies (neighborhood contrastive learning) still have significant limitations: (1) The search range for positives is fixed to the first-order neighborhood, which is overly restrictive and makes it difficult to fully explore the latent similarity between nodes; (2) Excessive focus is placed on intra-view connection relationships while ignoring the mining of inter-view structural consistency, which tends to lead to the omission of positives with stable similarity to the anchor or the misclassification of low-similarity nodes as positives. To address the above limitations, this paper proposes a Multi-view Graph Contrastive Clustering via Consensus Constraint (MGC<sup>4</sup>). Specifically, the algorithm first leverages inter-view structural consistency to identify the consensus neighbors of anchors and their cross-view counterparts as high-quality positives. Second, it assigns adaptive contrastive weights according to the cross-view average graph distance between the positives and the anchor, distinguishing the importance of different positives. Extensive experiments on multiple real-world datasets demonstrate that MGC<sup>4</sup> consistently outperforms current state-of-the-art baselines in both clustering accuracy and robustness.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"676 ","pages":"Article 133069"},"PeriodicalIF":6.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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