Neural Networks最新文献_第3页

MGNet: RGBT tracking via cross-modality cross-region mutual guidance MGNet：通过跨模式、跨区域的相互指导来跟踪同性恋

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-06-14 DOI: 10.1016/j.neunet.2025.107707

Jianming Zhang , Jing Yang , Yu Qin , Zhu Xiao , Jin Wang

{"title":"MGNet: RGBT tracking via cross-modality cross-region mutual guidance","authors":"Jianming Zhang , Jing Yang , Yu Qin , Zhu Xiao , Jin Wang","doi":"10.1016/j.neunet.2025.107707","DOIUrl":"10.1016/j.neunet.2025.107707","url":null,"abstract":"<div><div>Compared to single modal object tracking, the main challenge in RGBT tracking lies in effectively fusing features from both modalities. However, many existing methods neglect the dependence of distinct regions from different modalities, instead only considering that of identical regions, which fails to capture the cross-modal cross-regional relationships. In other words, they do not leverage the mutual guidance between different regions of different modalities. To address this limitation, we propose a novel RGBT tracking network, MGNet, which employs dual-stage attention and multi-scale feature fusion. The network includes the Cross-modality Cross-region Dual-stage Attention (CCDA) module and the Multi-scale Intra-region Feature Fusion (MIFF) module. The CCDA module processes features in two stages to preserve the unique features of identical region of different modalities, and then achieves mutual guidance across them. Specifically, in the first stage, features from different regions of different modalities are combined into a mixed representation, maintaining the distinct features of each region. In the second stage, attention mechanisms are applied to the mixed representation, facilitating cross-modality cross-region mutual guidance. Additionally, the MIFF module can perceive feature changes at multiple scales, ensuring effective fusion within each region. Our method achieves superior performance on three RGBT benchmark datasets (GTOT, RGBT234, and LasHeR) while running at 75 FPS, demonstrating both high accuracy and real-time performance.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"190 ","pages":"Article 107707"},"PeriodicalIF":6.0,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307407","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

Physics-informed neural networks for solving inverse problems in phase field models 求解相场模型反问题的物理信息神经网络

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-06-14 DOI: 10.1016/j.neunet.2025.107665

B.R. Zhao , D.K. Sun , H. Wu , C.J. Qin , Q.G. Fei

{"title":"Physics-informed neural networks for solving inverse problems in phase field models","authors":"B.R. Zhao , D.K. Sun , H. Wu , C.J. Qin , Q.G. Fei","doi":"10.1016/j.neunet.2025.107665","DOIUrl":"10.1016/j.neunet.2025.107665","url":null,"abstract":"<div><div>The integration of materials science with Physics-Informed Neural Networks (PINNs) is critical for understanding and predicting material properties, especially through the study of inverse problems. However, much of the current research in materials science primarily focuses on applying PINNs to forward problems or improving prediction accuracy. This paper shifts the focus to inverse problems related to numerical simulation modeling, encompassing diffusion, flow, and phase transition problems through PINNs. By constructing a neural network that integrates data-driven and physics-driven modules, this study uncovers the underlying physical laws embedded within the data. More importantly, this work further validates the applicability of PINNs in the inversion of key anisotropic material parameters, with benchmark anisotropic function inversion results demonstrating a high degree of consistency between predicted and theoretical values. Additionally, this study extends the application of PINNs to multi-physics coupled systems by addressing inverse problems associated with the governing equations of phase field, temperature field, and flow field, thereby enabling parameter inversion under multi-physics conditions. This novel approach addressing inverse problems and the inversion of critical material parameters provides new perspectives, demonstrating the potential of integrating numerical simulation data and deep learning, further deepening the research on PINNs in material science.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"190 ","pages":"Article 107665"},"PeriodicalIF":6.0,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306861","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

Scalable one-pass multi-view clustering with tensorized multiscale bipartite graphs fusion 基于张张化多尺度二部图融合的可伸缩单次多视图聚类

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-06-14 DOI: 10.1016/j.neunet.2025.107669

Fei Wang, Gui-Fu Lu

{"title":"Scalable one-pass multi-view clustering with tensorized multiscale bipartite graphs fusion","authors":"Fei Wang, Gui-Fu Lu","doi":"10.1016/j.neunet.2025.107669","DOIUrl":"10.1016/j.neunet.2025.107669","url":null,"abstract":"<div><div>In the existing multi-view clustering task, anchor-based methods are widely used for large-scale data processing to reduce computational complexity and achieve satisfactory results. However, most existing anchor-based algorithms generate a single-scale bipartite graph for each view, limiting a more accurate representation of the original data. Moreover, these algorithms typically require further clustering processing, and the contribution of each view to the final clustering result is static, lacking dynamic adjustment based on the data characteristics. To address the above issues, we introduce an innovative multi-view clustering method called Scalable One-pass Multi-View Clustering with Tensorized Multiscale Bipartite Graphs Fusion (SOMVC/TMBGF). Specifically, we initially generate multiple scales of bipartite graphs for each view and adaptively fuse them to obtain a partition matrix, thereby fully leveraging the structural information of the original data for a more accurate representation. Subsequently, we combine the partition matrices from each view into a tensor constrained with Tensor Schatten <span><math><mi>p</mi></math></span>-norm, capturing the higher-order correlations and complementary information between views. Finally, to enhance clustering performance, we integrate partition matrix learning and clustering into a unified framework, dynamically adjusting the contribution of each view’s partition matrix through weighted spectral rotation to obtain the final clustering result. Experimental results show that SOMVC/TMBGF outperforms existing methods significantly in both clustering performance and computational efficiency, demonstrating its advantage in handling large-scale multi-view data.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"190 ","pages":"Article 107669"},"PeriodicalIF":6.0,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307406","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

Diffusion policy distillation for offline reinforcement learning 离线强化学习的扩散策略蒸馏

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-06-14 DOI: 10.1016/j.neunet.2025.107694

Jiazhi Zhang , Yuhu Cheng , C.L. Philip Chen , Hengrui Zhang , Xuesong Wang

{"title":"Diffusion policy distillation for offline reinforcement learning","authors":"Jiazhi Zhang , Yuhu Cheng , C.L. Philip Chen , Hengrui Zhang , Xuesong Wang","doi":"10.1016/j.neunet.2025.107694","DOIUrl":"10.1016/j.neunet.2025.107694","url":null,"abstract":"<div><div>Offline reinforcement learning aims to learn a well-performing target policy from a static empirical dataset. Leveraging its powerful distribution expression capabilities, the diffusion model has been widely adopted as a type of policy in offline reinforcement learning. However, sampling a single action from diffusion policy necessitates a multi-step denoise process, which results in slow decision-making speed and poses challenges for application in real-time control tasks. Inspired by the teacher–student mechanism in human learning, this paper proposes a diffusion policy distillation (DPD) framework, which employs a deterministic policy to distill the target policy induced by the diffusion model. Although the deterministic policy cannot express the complex behavior policy induced by the empirical dataset properly, it can effectively learn a relevant target policy. Moreover, since the distillated deterministic policy is one-step, it avoids the need for iterative denoising, thereby inheriting the performance of the target policy while effectively improving the decision-making speed. DPD is plug-and-play and thus can be combined with offline reinforcement learning methods based on diffusion policy. Experimental results on D4RL Gym-MuJoCo datasets indicate that the distillation policy can achieve a higher normalized score than the original policy with a lower standard deviation, and improve the decision-making speed by over 10 times.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"190 ","pages":"Article 107694"},"PeriodicalIF":6.0,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306931","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

Convergent adaptive control based prescribed-time synchronization of switched fuzzy competitive network systems with time-varying delays 时变时滞交换模糊竞争网络系统的定时同步收敛自适应控制

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-06-14 DOI: 10.1016/j.neunet.2025.107691

Dongdong Gao , Fanchao Kong , Tingwen Huang

{"title":"Convergent adaptive control based prescribed-time synchronization of switched fuzzy competitive network systems with time-varying delays","authors":"Dongdong Gao , Fanchao Kong , Tingwen Huang","doi":"10.1016/j.neunet.2025.107691","DOIUrl":"10.1016/j.neunet.2025.107691","url":null,"abstract":"<div><div>This paper addresses the prescribed-time control problem for discontinuous fuzzy neutral-type competitive neural networks (FNTCNNs) featuring switchings and time-varying delays. Notably, FNTCNNs constitute a generalized class of singularly perturbed Filippov systems. The establishment of a prescribed-time stability lemma for time-varying delay singularly perturbed systems remains a critical yet unresolved challenge. To address this, we first develop a novel prescribed-time stability lemma for singularly perturbed Filippov systems using adjustment functions, the comparison principle, and inequality techniques. This is achieved through the application of the one-norm and the introduction of a new stability definition for such systems. Considering the switching law inherent in FNTCNNs, we achieve prescribed-time stabilization control by designing adaptive prescribed-time control strategies, employing differential inclusion theory and Filippov’s solution framework. The proposed adaptive control strategies demonstrate convergence properties, ensuring that both the control strategies and system state variables converge to zero within the same prescribed-time interval. These newly developed strategies offer significant advantages over existing approaches. Finally, we validate our principal results through numerical simulations of second-order multi-agent systems subject to discontinuous disturbances.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"190 ","pages":"Article 107691"},"PeriodicalIF":6.0,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297358","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 general analog solver of linear and quadratic programming in one step 线性和二次规划的一般模拟求解器在一步

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-06-13 DOI: 10.1016/j.neunet.2025.107698

Sichun Du, Yu Dong, Pingdan Xiao, Zhengmiao Wei, Qinghui Hong

{"title":"A general analog solver of linear and quadratic programming in one step","authors":"Sichun Du, Yu Dong, Pingdan Xiao, Zhengmiao Wei, Qinghui Hong","doi":"10.1016/j.neunet.2025.107698","DOIUrl":"10.1016/j.neunet.2025.107698","url":null,"abstract":"<div><div>Real-time solving of linear programming (LP) and quadratic programming (QP) problems faces critical demand across engineering and scientific domains. Conventional numerical approaches suffer from exponential growth in computational complexity as problem dimensionality and structural complexity increase. To address this challenge, we present a general analog solver grounded in neurodynamic principles, achieving closed-form solutions for both LP and QP through physical-level computation in one step. The proposed solver achieves the solution of LP/QP problems under diverse constraints through configurable interconnections of modular analog circuits. The analog computing architecture based on continuous-time dynamics leverages its inherent parallelism and sub-microsecond convergence properties to enhance the efficiency of optimization problem solving. Through five PSPICE simulation test experiments, the proposed QP solver achieved an average solution accuracy exceeding 99.9%, with robustness metrics maintaining over 93% precision when subjected to circuit nonidealities, including noise, parasitic resistance, and device deviation. Comparative analysis shows that the proposed solver demonstrates 173.572<span><math><mo>×</mo></math></span>, 115.871<span><math><mo>×</mo></math></span>, 8.387<span><math><mo>×</mo></math></span>, 3.241<span><math><mo>×</mo></math></span>, 21.623<span><math><mo>×</mo></math></span>, respectively, acceleration over traditional QP solvers.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"190 ","pages":"Article 107698"},"PeriodicalIF":6.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312914","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

LEESDFormer: A lightweight unsupervised CNN-Transformer-based curve estimation network for low-light image enhancement, exposure suppression, and denoising LEESDFormer：一种轻量级的无监督cnn - transformer曲线估计网络，用于弱光图像增强、曝光抑制和去噪

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-06-13 DOI: 10.1016/j.neunet.2025.107764

Rui He, Xuanhe Li, Jian Wu

{"title":"LEESDFormer: A lightweight unsupervised CNN-Transformer-based curve estimation network for low-light image enhancement, exposure suppression, and denoising","authors":"Rui He, Xuanhe Li, Jian Wu","doi":"10.1016/j.neunet.2025.107764","DOIUrl":"10.1016/j.neunet.2025.107764","url":null,"abstract":"<div><div>Current low-light image enhancement methods mainly focus on improving the low-light regions within images. However, they often fail to adequately consider the impact of mixed exposures and noise on the images, resulting in suboptimal enhancement results and even loss of some detailed information. Moreover, these methods predominantly rely on convolutional neural networks (CNNs), which have inherent constraints in capturing long-range dependencies and global information. To address these issues, this paper introduces LEESDFormer, the first unsupervised low-light image enhancement method based on CNN-Transformer. Firstly, we propose a Low-light Enhancement and Exposure Suppression S-shaped curve (LEES-S curve), which simplifies the complex challenge of low-light enhancement and exposure suppression into a simpler curve estimation task, thus substantially reducing the task's complexity. LEESDFormer iterates the LEES-S curve through the Low-light Image Enhancement and Exposure Suppression Module (LEESM), thereby achieving desired enhancement effects. Subsequently, the Image Denoising Module (IDM) is employed to denoise the enhanced images. Extensive experiments demonstrate that our method exhibits excellent robustness, generalization capabilities, and visual effects compared to state-of-the-art unsupervised low-light image enhancement methods, even outperforming some supervised learning approaches. Notably, our method achieves a Peak Signal-to-Noise Ratio (PSNR) of <strong>21 dB</strong> on the LOL-v2-real dataset, demonstrating its superior enhancement performance and denoising capability. Furthermore, LEESDFormer is simple and efficient, with only <strong>65 K</strong> parameters, and processes each image in merely <strong>8 ms</strong>, making it deployable on resource-limited devices and having significant practical value.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"190 ","pages":"Article 107764"},"PeriodicalIF":6.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470345","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

Transfer learning-motivated intelligent fault diagnosis framework for cross-domain knowledge distillation 基于迁移学习的跨领域知识升华智能故障诊断框架

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-06-13 DOI: 10.1016/j.neunet.2025.107699

Penghao Wu , Engang Tian , Hongfeng Tao , Yiyang Chen

引用次数: 0

Burst denoising transformer with multi-task optical flow estimation 基于多任务光流估计的突发去噪变压器

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-06-13 DOI: 10.1016/j.neunet.2025.107696

Sicheng Pan, Yingming Li

引用次数: 0

Structure regularized consensus dynamic anchor graph learning for incomplete multi-view clustering 不完全多视图聚类的结构正则化一致性动态锚图学习

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-06-13 DOI: 10.1016/j.neunet.2025.107765

Bing Hu , Lixin Han , Yi Xu , Chang Tang , Jun Zhu , Gui-Fu Lu

{"title":"Structure regularized consensus dynamic anchor graph learning for incomplete multi-view clustering","authors":"Bing Hu , Lixin Han , Yi Xu , Chang Tang , Jun Zhu , Gui-Fu Lu","doi":"10.1016/j.neunet.2025.107765","DOIUrl":"10.1016/j.neunet.2025.107765","url":null,"abstract":"<div><div>Dynamic anchor graph-based incomplete multi-view clustering (IMVC) algorithms have garnered extensive research attention in recent years owing to their relatively low time complexity. However, these algorithms suffer from two limitations. First, most of the existing methods disregard the structural information of the original feature spaces. Second, nearly all the current approaches emphasize the importance of each view while overlooking the weights of each feature. To address these issues, we propose an algorithm named SRCDAGL-IMC. Specifically, we use the structural information of all the views as regularization terms to constrain the relationships between different pairs in the consensus anchor graph. Moreover, we add coefficients to each sample to measure their individual importance in their own view, and we simultaneously recover the missing features. Thus, the learning of the consensus anchor graph, and the recovery of the missing features, mutually promote each other. We also propose an effective alternating optimization method. Experiments on six public datasets show that our algorithm outperforms the state-of-the-art matrix factorization-based incomplete multi-view algorithms in terms of accuracy, normalized mutual information, purity. Our code is publicly available on <span><span>https://github.com/BingHuAhpu/SRCDAGL-IMC</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"190 ","pages":"Article 107765"},"PeriodicalIF":6.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312739","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