IEEE transactions on neural networks and learning systems最新文献_第8页

IEEE Transactions on Neural Networks and Learning Systems Publication Information 电气和电子工程师学会神经网络与学习系统论文集》（IEEE Transactions on Neural Networks and Learning Systems）出版信息

IF 10.2 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-09-03 DOI: 10.1109/TNNLS.2024.3442456

引用次数: 0

Structured Deep Neural Network-Based Backstepping Trajectory Tracking Control for Lagrangian Systems. 基于结构化深度神经网络的拉格朗日系统后退轨迹跟踪控制。

IF 10.2 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-09-02 DOI: 10.1109/TNNLS.2024.3445976

Jiajun Qian, Liang Xu, Xiaoqiang Ren, Xiaofan Wang

{"title":"Structured Deep Neural Network-Based Backstepping Trajectory Tracking Control for Lagrangian Systems.","authors":"Jiajun Qian, Liang Xu, Xiaoqiang Ren, Xiaofan Wang","doi":"10.1109/TNNLS.2024.3445976","DOIUrl":"https://doi.org/10.1109/TNNLS.2024.3445976","url":null,"abstract":"Deep neural networks (DNNs) are increasingly being used to learn controllers due to their excellent approximation capabilities. However, their black-box nature poses significant challenges to closed-loop stability guarantees and performance analysis. In this brief, we introduce a structured DNN-based controller for the trajectory tracking control of Lagrangian systems using backing techniques. By properly designing neural network structures, the proposed controller can ensure closed-loop stability for any compatible neural network parameters. In addition, improved control performance can be achieved by further optimizing neural network parameters. Besides, we provide explicit upper bounds on tracking errors in terms of controller parameters, which allows us to achieve the desired tracking performance by properly selecting the controller parameters. Furthermore, when system models are unknown, we propose an improved Lagrangian neural network (LNN) structure to learn the system dynamics and design the controller. We show that in the presence of model approximation errors and external disturbances, the closed-loop stability and tracking control performance can still be guaranteed. The effectiveness of the proposed approach is demonstrated through simulations.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142119704","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

Estimator-Based Reinforcement Learning Consensus Control for Multiagent Systems With Discontinuous Constraints. 具有不连续约束条件的多代理系统的基于估计器的强化学习共识控制

IF 10.2 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-09-02 DOI: 10.1109/TNNLS.2024.3445880

Ao Luo, Hui Ma, Hongru Ren, Hongyi Li

{"title":"Estimator-Based Reinforcement Learning Consensus Control for Multiagent Systems With Discontinuous Constraints.","authors":"Ao Luo, Hui Ma, Hongru Ren, Hongyi Li","doi":"10.1109/TNNLS.2024.3445880","DOIUrl":"10.1109/TNNLS.2024.3445880","url":null,"abstract":"This article focuses on the optimal consensus control problem for multiagent systems (MASs) with discontinuous constraints. The case of discontinuous constraints is a particular instance of state constraints, which has been studied less but occurs in many practical situations. Due to the discontinuous constraint boundaries, the traditional barrier function-based backstepping methods cannot be used directly. In response to this thorny problem, a novel constraint boundary reconstruction technique is proposed by designing a class of switch-like functions. The technique can convert discontinuous constraint boundaries into continuous ones, and it strictly proves that when the states satisfy the transformed constraint boundaries, the original constraints are also absolutely fulfilled. Meanwhile, with the aid of the barrier function and distributed event-triggered estimator, an improved coordinate transformation is constructed, which can remove the \"feasibility condition\" and simplify the controller design. In addition, by introducing prediction error and revised term into the learning process of neural networks (NNs), the optimal consensus problem is resolved by constructing a modified reinforcement learning strategy. Finally, the stability of the MASs is testified through the Lyapunov stability theory, and a simulation example verifies the effectiveness of the proposed method.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142119703","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 Soft Label Learning Based on Orthogonal NMF. 基于正交 NMF 的张量软标签学习

IF 10.2 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-09-02 DOI: 10.1109/TNNLS.2024.3442435

Fangfang Li, Quanxue Gao, Qianqian Wang, Ming Yang, Cheng Deng

{"title":"Tensorized Soft Label Learning Based on Orthogonal NMF.","authors":"Fangfang Li, Quanxue Gao, Qianqian Wang, Ming Yang, Cheng Deng","doi":"10.1109/TNNLS.2024.3442435","DOIUrl":"https://doi.org/10.1109/TNNLS.2024.3442435","url":null,"abstract":"Recently, a strong interest has been in multiview high-dimensional data collected through cross-domain or various feature extraction mechanisms. Nonnegative matrix factorization (NMF) is an effective method for clustering these high-dimensional data with clear physical significance. However, existing multiview clustering based on NMF only measures the difference between the elements of the coefficient matrix without considering the spatial structure relationship between the elements. And they often require postprocessing to achieve clustering, making the algorithms unstable. To address this issue, we propose minimizing the Schatten p -norm of the tensor, which consists of a coefficient matrix of different views. This approach considers each element's spatial structure in the coefficient matrices, crucial for effectively capturing complementary information presented in different views. Furthermore, we apply orthogonal constraints to the cluster index matrix to make it sparse and provide a strong interpretation of the clustering. This allows us to obtain the cluster label directly without any postprocessing. To distinguish the importance of different views, we utilize adaptive weights to assign varying weights to each view. We introduce an unsupervised optimization scheme to solve and analyze the computational complexity of the model. Through comprehensive evaluations of six benchmark datasets and comparisons with several multiview clustering algorithms, we empirically demonstrate the superiority of our proposed method.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142119705","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 Comprehensive Study on Self-Learning Methods and Implications to Autonomous Driving. 关于自学方法及其对自动驾驶影响的综合研究。

IF 10.2 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-09-02 DOI: 10.1109/TNNLS.2024.3440498

Jiaming Xing, Dengwei Wei, Shanghang Zhou, Tingting Wang, Yanjun Huang, Hong Chen

{"title":"A Comprehensive Study on Self-Learning Methods and Implications to Autonomous Driving.","authors":"Jiaming Xing, Dengwei Wei, Shanghang Zhou, Tingting Wang, Yanjun Huang, Hong Chen","doi":"10.1109/TNNLS.2024.3440498","DOIUrl":"https://doi.org/10.1109/TNNLS.2024.3440498","url":null,"abstract":"As artificial intelligence (AI) has already seen numerous successful applications, the upcoming challenge lies in how to realize artificial general intelligence (AGI). Self-learning algorithms can autonomously acquire knowledge and adapt to new, demanding applications, recognized as one of the most effective techniques to overcome this challenge. Although many related studies have been conducted, there is still no comprehensive and systematic review available, nor well-founded recommendations for the application of autonomous intelligent systems, especially autonomous driving. As a result, this article comprehensively analyzes and classifies self-learning algorithms into three categories: broad self-learning, narrow self-learning, and limited self-learning. These categories are used to describe the popular usage, the most promising techniques, and the current status of hybridization with self-supervised learning. Then, the narrow self-learning is divided into three parts based on the self-learning realization path: sample self-learning, model self-learning, and self-learning architecture. For each method, this article discusses in detail its self-learning capacity, challenges, and applications to autonomous driving. Finally, the future research directions of self-learning algorithms are pointed out. It is expected that this study has the potential to eventually contribute to revolutionizing autonomous driving technology.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142119701","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

Anomaly Detection in Hyperspectral Images Using Adaptive Graph Frequency Location. 利用自适应图频定位在高光谱图像中进行异常检测

IF 10.2 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-09-02 DOI: 10.1109/TNNLS.2024.3449573

Bing Tu, Xianchang Yang, Baoliang He, Yunyun Chen, Jun Li, Antonio Plaza

{"title":"Anomaly Detection in Hyperspectral Images Using Adaptive Graph Frequency Location.","authors":"Bing Tu, Xianchang Yang, Baoliang He, Yunyun Chen, Jun Li, Antonio Plaza","doi":"10.1109/TNNLS.2024.3449573","DOIUrl":"https://doi.org/10.1109/TNNLS.2024.3449573","url":null,"abstract":"Graph theory-based techniques have recently been adopted for anomaly detection in hyperspectral images (HSIs). However, these methods rely excessively on the relational structure within the constructed graphs and tend to downplay the importance of spectral features in the original HSI. To address this issue, we introduce graph frequency analysis to hyperspectral anomaly detection (HAD), which can serve as a natural tool for integrating graph structure and spectral features. We treat anomaly detection as a problem of graph frequency location, achieved by constructing a beta distribution-based graph wavelet space, where the optimal wavelet can be identified adaptively for anomaly detection. Initially, a high-dimensional, undirected, unweighted graph is built using the pixels in the HSI as vertices. By leveraging the observation of energy shifting to higher frequencies caused by anomalies, we can dynamically pinpoint the specific Beta wavelet associated with the anomalies' high-frequency content to accurately extract anomalies in the context of HSIs. Furthermore, we introduce a novel entropy definition to address the frequency location problem in an adaptive manner. Experimental results from seven real HSIs validate the remarkable detection performance of our newly proposed approach when compared to various state-of-the-art anomaly detection methods.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142119702","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 Virtual-Label-Based Hierarchical Domain Adaptation Method for Time-Series Classification. 基于虚拟标签的时间序列分类分层域适应方法

IF 10.2 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-08-28 DOI: 10.1109/TNNLS.2024.3445879

Wenmian Yang, Lizhi Cheng, Mohamed Ragab, Min Wu, Sinno Jialin Pan, Zhenghua Chen

{"title":"A Virtual-Label-Based Hierarchical Domain Adaptation Method for Time-Series Classification.","authors":"Wenmian Yang, Lizhi Cheng, Mohamed Ragab, Min Wu, Sinno Jialin Pan, Zhenghua Chen","doi":"10.1109/TNNLS.2024.3445879","DOIUrl":"https://doi.org/10.1109/TNNLS.2024.3445879","url":null,"abstract":"Unsupervised domain adaptation (UDA) is becoming a prominent solution for the domain-shift problem in many time-series classification tasks. With sequence properties, time-series data contain both local and sequential features, and the domain shift exists in both features. However, conventional UDA methods usually cannot distinguish those two features but mix them into one variable for direct alignment, which harms the performance. To address this problem, we propose a novel virtual-label-based hierarchical domain adaptation (VLH-DA) approach for time-series classification. Specifically, we first slice the original time-series data and introduce virtual labels to represent the type of each slice (called local patterns). With the help of virtual labels, we decompose the end-to-end (i.e., signal to time-series label) time-series task into two parts, i.e., signal sequence to local pattern sequence and local pattern sequence to time-series label. By decomposing the complex time-series UDA task into two simpler subtasks, the local features and sequential features can be aligned separately, making it easier to mitigate distribution discrepancies. Experiments on four public time-series datasets demonstrate that our VLH-DA outperforms all state-of-the-art (SOTA) methods.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142086091","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

Unsupervised Abundance Matrix Reconstruction Transformer-Guided Fractional Attention Mechanism for Hyperspectral Anomaly Detection. 用于高光谱异常检测的无监督丰度矩阵重构变换器引导的分数注意机制

IF 10.2 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-08-28 DOI: 10.1109/TNNLS.2024.3437731

Si-Sheng Young, Chia-Hsiang Lin, Zi-Chao Leng

{"title":"Unsupervised Abundance Matrix Reconstruction Transformer-Guided Fractional Attention Mechanism for Hyperspectral Anomaly Detection.","authors":"Si-Sheng Young, Chia-Hsiang Lin, Zi-Chao Leng","doi":"10.1109/TNNLS.2024.3437731","DOIUrl":"https://doi.org/10.1109/TNNLS.2024.3437731","url":null,"abstract":"Hyperspectral anomaly detection (HAD), a challenging inverse problem, has found numerous scientific applications. Although extant HAD algorithms have achieved remarkable results, there are still several issues remained unresolved: 1) low spatial resolution (and spectral redundancy) in typical hyperspectral images prevents effectively distinguishing the abnormal pixels from those normal ones and 2) the reconstruction from existing residual-based frameworks would not completely remove anomaly effects, making the detection solely from the residual impractical. In this article, we propose a novel HAD method, termed transformer-guided fractional attention within the abundance domain (TGFA-AD), which substitutes raw input image with the abundance matrix obtained via blind source separation (BSS). First, the proposed abundance spatial-channel reconstruction transformer (ASCR-Former) is customized for rebuilding the abundance matrix. According to the image self-similarity, the abundance is patch-wisely encoded with class (CLS) tokens. The transformer encoders intensify the spatial and channel characteristics between tokens for reconstructing the abundance, followed by deriving the initial detection from the abundance residual matrix. Second, a novel fractional abundance attention (FAA) mechanism is proposed, where the attention weights coming from a specific linear combination of abundances are guided by the initial detection with convex [Formula: see text] -quadratic norm. Finally, the fractional convolution is incorporated to fuse the abundance and residual into the fractional feature for yielding the final detection result. Real data experiments quantitatively and qualitatively exhibit the state-of-the-art performance of TGFA-AD.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142086092","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

DSANet: Dynamic and Structure-Aware GCN for Sparse and Incomplete Point Cloud Learning. DSANet：用于稀疏和不完整点云学习的动态和结构感知 GCN。

IF 10.2 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-08-27 DOI: 10.1109/TNNLS.2024.3439706

Yushi Li, George Baciu, Rong Chen, Chenhui Li, Hao Wang, Yushan Pan, Weiping Ding

{"title":"DSANet: Dynamic and Structure-Aware GCN for Sparse and Incomplete Point Cloud Learning.","authors":"Yushi Li, George Baciu, Rong Chen, Chenhui Li, Hao Wang, Yushan Pan, Weiping Ding","doi":"10.1109/TNNLS.2024.3439706","DOIUrl":"https://doi.org/10.1109/TNNLS.2024.3439706","url":null,"abstract":"Learning 3-D structures from incomplete point clouds with extreme sparsity and random distributions is a challenge since it is difficult to infer topological connectivity and structural details from fragmentary representations. Missing large portions of informative structures further aggravates this problem. To overcome this, a novel graph convolutional network (GCN) called dynamic and structure-aware NETwork (DSANet) is presented in this article. This framework is formulated based on a pyramidic auto-encoder (AE) architecture to address accurate structure reconstruction on the sparse and incomplete point clouds. A PointNet-like neural network is applied as the encoder to efficiently aggregate the global representations of coarse point clouds. On the decoder side, we design a dynamic graph learning module with a structure-aware attention (SAA) to take advantage of the topology relationships maintained in the dynamic latent graph. Relying on gradually unfolding the extracted representation into a sequence of graphs, DSANet is able to reconstruct complicated point clouds with rich and descriptive details. To associate analogous structure awareness with semantic estimation, we further propose a mechanism, called structure similarity assessment (SSA). This method allows our model to surmise semantic homogeneity in an unsupervised manner. Finally, we optimize the proposed model by minimizing a new distortion-aware objective end-to-end. Extensive qualitative and quantitative experiments demonstrate the impressive performance of our model in reconstructing unbroken 3-D shapes from deficient point clouds and preserving semantic relationships among different regional structures.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142080150","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

Multidimensional Measure Matching for Crowd Counting. 人群计数的多维测量匹配

IF 10.2 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-08-27 DOI: 10.1109/TNNLS.2024.3435854

Hui Lin, Xiaopeng Hong, Zhiheng Ma, Yaowei Wang, Deyu Meng

{"title":"Multidimensional Measure Matching for Crowd Counting.","authors":"Hui Lin, Xiaopeng Hong, Zhiheng Ma, Yaowei Wang, Deyu Meng","doi":"10.1109/TNNLS.2024.3435854","DOIUrl":"https://doi.org/10.1109/TNNLS.2024.3435854","url":null,"abstract":"This article addresses the challenge of scale variations in crowd-counting problems from a multidimensional measure-theoretic perspective. We start by formulating crowd counting as a measure-matching problem, based on the assumption that discrete measures can express the scattered ground truth and the predicted density map. In this context, we introduce the Sinkhorn counting loss and extend it to the semi-balanced form, which alleviates the problems including entropic bias, distance destruction, and amount constraints. We then model the measure matching under the multidimensional space, in order to learn the counting from both location and scale. To achieve this, we extend the traditional 2-D coordinate support to 3-D, incorporating an additional axis to represent scale information, where a pyramid-based structure will be leveraged to learn the scale value for the predicted density. Extensive experiments on four challenging crowd-counting datasets, namely, ShanghaiTech A, UCF-QNRF, JHU ++ , and NWPU have validated the proposed method. Code is released at https://github.com/LoraLinH/Multidimensional-Measure-Matching-for-Crowd-Counting.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142080152","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