Neural Networks最新文献_第10页

Attentional dual-stream interactive perception network for efficient infrared small aerial target detection 基于注意力双流交互感知网络的红外空中小目标高效检测

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-06-01 Epub Date: 2026-01-08 DOI: 10.1016/j.neunet.2026.108563

Lihao Zhou, Huawei Wang

Drones and other flying objects can be regarded as small targets from a long-distance perspective. Considering the occlusion and interference caused by the external environment, the infrared detection methods are adopted to help identify and manage small aerial targets. However, remote infrared imaging often leads to small target feature detail loss. And the general methods have low detection efficiency, difficult to deeply extract target features. To better address the above problems, we propose an attentional dual-stream interactive perception network (ADIPNet) in this paper. Based on dual-stream U-Net, ADIPNet mainly combines the multi-patch series-parallel attention module (MSPA), edge anchoring module with regret (EAR), context scene perception module (CSP) and dual-stream interaction fusion module (DSIF). MSPA manually constructs the weight of patch regions at multiple scales and then performs the nested self-attention so as to fully mine global target information. EAR unites two types of global features using local mapping and matrix product, which helps accurately capture small target edge. CSP exchanges context information multiple times and conducts mutual complementation of semantic scenarios to enhances the perception of small target features. Finally, DSIF conducts cross attention for high-level encoded features on double U-Nets, further improving the network’s understanding of complex scenario information. The proposed ADIPNet alleviates the insufficient feature extraction of infrared small targets. Compared with other state-of-the-art methods, mIoU respectively reaches 80.52% and 72.54% on two large infrared datasets. It achieves more accurate detection of small aerial targets with low operating cost, possessing potential application prospect in various infrared surveillance systems.

从远距离的角度来看，无人机和其他飞行物可以被视为小目标。考虑到外界环境的遮挡和干扰，采用红外探测方法对小型空中目标进行识别和管理。然而，远程红外成像往往会导致小目标特征细节的丢失。而一般方法检测效率低，难以深度提取目标特征。为了更好地解决上述问题，本文提出了一种注意力双流交互感知网络（ADIPNet）。ADIPNet基于双流U-Net，主要结合多补丁串并联注意模块（MSPA）、悔恨边缘锚定模块（EAR）、情境场景感知模块（CSP）和双流交互融合模块（DSIF）。MSPA在多个尺度上手动构建patch区域的权值，然后进行嵌套自关注，以充分挖掘全局目标信息。EAR利用局部映射和矩阵积将两种类型的全局特征结合起来，有助于准确捕获小目标边缘。CSP多次交换上下文信息，进行语义场景的相互补充，增强对小目标特征的感知。最后，DSIF对双U-Nets的高级编码特征进行交叉关注，进一步提高了网络对复杂场景信息的理解能力。所提出的ADIPNet缓解了红外小目标特征提取不足的问题。与其他最先进的方法相比，在两个大型红外数据集上mIoU分别达到80.52%和72.54%。该方法实现了对小型空中目标更精确的探测，运行成本低，在各种红外监视系统中具有潜在的应用前景。

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引用次数: 0

CocoAdapter: Efficient end-to-end temporal action detection via self-constrained multi-cognitive adapters CocoAdapter：通过自我约束的多认知适配器进行高效的端到端临时动作检测。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-06-01 Epub Date: 2026-01-01 DOI: 10.1016/j.neunet.2025.108531

Lizao Zhang , Qiuhong Tian , Junxiao Ning , Yihan Yuan , Ziyu Yang , Yang Yu

End-to-end training in temporal action detection (TAD) has shown great potential for performance improvement by jointly optimizing the video encoder and action classification head. However, memory bottlenecks have limited the performance of end-to-end TAD. To alleviate the memory overhead during training, this paper explores the application of adapters in TAD and proposes a specialized TAD-oriented self-constraint multi-cognitive adapter (CocoAdapter). Based on CocoAdapter, we construct a novel baseline, CocoTad. Our proposed CocoAdapter utilizes self-constraint projection layers to adjust multiple cognitive convolutional groups based on network depth, enabling a fine-tuning process tailored to the TAD task. As a result, the network only needs to update the parameters in CocoAdapter to achieve end-to-end training, significantly reducing memory consumption during training. We evaluate our model on four representative datasets. Experimental results demonstrate that our proposed CocoTad surpasses previous state-of-the-art methods in terms of mAP.

通过对视频编码器和动作分类头的联合优化，端到端训练在时间动作检测（TAD）中显示出巨大的性能提升潜力。然而，内存瓶颈限制了端到端TAD的性能。为了减轻训练过程中的内存开销，本文探讨了适配器在TAD中的应用，提出了一种专门的面向TAD的自约束多认知适配器（CocoAdapter）。基于CocoAdapter，我们构建了一个新的基线——CocoTad。我们提出的CocoAdapter利用自我约束投影层来调整基于网络深度的多个认知卷积组，从而实现针对TAD任务的微调过程。因此，网络只需要更新CocoAdapter中的参数就可以实现端到端的训练，大大减少了训练过程中的内存消耗。我们在四个代表性数据集上评估了我们的模型。实验结果表明，我们提出的CocoTad在mAP方面超越了以前最先进的方法。

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引用次数: 0

Graph-enhanced dual low-rank correlation embedding for spatio-temporal EEG fusion in depression recognition 基于图增强双低秩相关嵌入的脑电时空融合抑郁症识别。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-06-01 Epub Date: 2026-01-17 DOI: 10.1016/j.neunet.2026.108609

Lu Zhang , Jisheng Dang , Shu Zhang , Wencheng Gan , Juan Wang , Bin Hu , Gang Feng , Hong Peng

Electroencephalography (EEG) signals contain rich spatiotemporal information reflecting brain activity, making them valuable for analyzing cognitive, emotional, and neurological disorders. However, effectively integrating these two types of information to capture both discriminative and complementary features remains a significant challenge. To address this, we propose a Graph-Enhanced Dual Low-Rank Correlation Embedding (GEDLCE) method, which integrates spatiotemporal EEG features to improve depression recognition. GEDLCE enforces low-rank constraints at both feature and sample levels, enabling extraction of shared latent factors across multiple feature sets. To preserve the intrinsic geometric structure of the data, GEDLCE employs two graph Laplacian terms to model local relationships in the sample space. Furthermore, GEDLCE introduces a graph embedding term that utilizes label information to enhance its discriminative capability. In addition, GEDLCE incorporates an enhanced correlation analysis to exploit inter-view correlations while reducing intra-view redundancy. Finally, GEDLCE jointly optimizes low-rank representations, correlation constraints, and graph embedding within a unified framework. Experiments on EEG datasets show that GEDLCE effectively captures critical information, achieves superior performance in depression recognition, and shows promise for early diagnosis and disease monitoring.

脑电图（EEG）信号包含丰富的反映大脑活动的时空信息，使其对分析认知、情绪和神经系统疾病有价值。然而，如何有效地整合这两种类型的信息以捕获判别和互补特征仍然是一个重大挑战。为了解决这个问题，我们提出了一种图增强双低秩相关嵌入（GEDLCE）方法，该方法集成了脑电图的时空特征，以提高抑郁症的识别能力。GEDLCE在特征和样本水平上强制执行低秩约束，从而能够跨多个特征集提取共享的潜在因素。为了保持数据固有的几何结构，GEDLCE采用两个图拉普拉斯项来模拟样本空间中的局部关系。此外，GEDLCE还引入了一个利用标签信息的图嵌入词来增强其判别能力。此外，GEDLCE结合了增强的相关性分析，以利用视图间的相关性，同时减少视图内冗余。最后，GEDLCE在统一的框架内共同优化了低秩表示、关联约束和图嵌入。在脑电数据集上的实验表明，GEDLCE能够有效捕获关键信息，在抑郁症识别方面取得了优异的成绩，在早期诊断和疾病监测方面具有广阔的应用前景。

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引用次数: 0

Two-hidden-layer ReLU neural networks and finite elements 两隐层ReLU神经网络与有限元

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-06-01 Epub Date: 2026-01-08 DOI: 10.1016/j.neunet.2026.108559

Pengzhan Jin

We point out that (continuous or discontinuous) piecewise linear functions on a convex polytope mesh can be represented by two-hidden-layer ReLU neural networks in a weak sense. In addition, the numbers of neurons of the two hidden layers required to weakly represent are accurately given based on the numbers of polytopes and hyperplanes involved in this mesh. The results naturally hold for constant and linear finite element functions. Such weak representation establishes a bridge between two-hidden-layer ReLU neural networks and finite element functions, and leads to a perspective for analyzing approximation capability of ReLU neural networks in L^p norm via finite element functions. Moreover, we discuss the strict representation for tensor finite element functions via the recent tensor neural networks.

我们指出凸多面体网格上的（连续或不连续）分段线性函数可以用两隐层ReLU神经网络在弱意义上表示。此外，基于该网格中涉及的多面体和超平面的数量，精确地给出了弱表示所需的两个隐藏层的神经元数量。结果自然适用于常数和线性有限元函数。这种弱表示在两隐层ReLU神经网络和有限元函数之间架起了一座桥梁，为利用有限元函数分析ReLU神经网络在Lp范数中的逼近能力提供了一个视角。此外，我们还利用最近的张量神经网络讨论了张量有限元函数的严格表示。

引用次数: 0

Multi-modal feature alignment networks for multi-label image classification 用于多标签图像分类的多模态特征对齐网络

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-06-01 Epub Date: 2026-01-20 DOI: 10.1016/j.neunet.2026.108629

Wenlan Kuang , Zhixin Li

Multi-label image classification is a classification task that assigns labels to multiple objects in an input image. Recent research ideas mainly focus on solving the semantic consistency of visual features and label features. However, since images contain complex scene content, the features captured by visual feature extraction networks based on grid or sequence representation may introduce redundant information or lack continuity when identifying irregular objects. In order to fully mine the visual information of complex objects in images and enhance the inter-modal interaction of images and labels, we introduce a flexible graph structure to explore the internal information of objects and design a multi-modal feature alignment (MMFA) network for multi-label image classification. To enhance the context awareness and semantic association of different patch regions, we propose a semantic-augmented interaction module that combines two kinds of visual semantic information with label embeddings for interactive learning. Finally, we refine the dependence between local intrinsic information and overall semantics by redefining semantic queries through semantically enhanced visual spatial features and graph aggregation features. Experiments on three large-scale public datasets: Microsoft COCO, Pascal VOC 2007 and NUS-WIDE demonstrate the effectiveness of our proposed MMFA and achieve state-of-the-art performance.

多标签图像分类是为输入图像中的多个对象分配标签的分类任务。目前的研究思路主要集中在解决视觉特征和标签特征的语义一致性问题上。然而，由于图像包含复杂的场景内容，基于网格或序列表示的视觉特征提取网络捕获的特征在识别不规则物体时可能会引入冗余信息或缺乏连续性。为了充分挖掘图像中复杂物体的视觉信息，增强图像与标签的多模态交互，我们引入了一种灵活的图结构来探索物体的内部信息，并设计了一个多模态特征对齐（MMFA）网络用于多标签图像分类。为了增强不同贴片区域的上下文感知和语义关联，我们提出了一种语义增强交互模块，该模块将两种视觉语义信息与标签嵌入相结合，用于交互学习。最后，我们通过语义增强的视觉空间特征和图聚合特征重新定义语义查询，从而细化局部内在信息与整体语义之间的依赖关系。在三个大型公共数据集上的实验：Microsoft COCO、Pascal VOC 2007和NUS-WIDE证明了我们提出的MMFA的有效性，并达到了最先进的性能。

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引用次数: 0

Self-supervised exceptional prototypical network for few-shot grading of gastric intestinal metaplasia 自监督异常原型网络对胃肠化生的少量分级

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-06-01 Epub Date: 2026-01-10 DOI: 10.1016/j.neunet.2026.108584

Xuanchi Chen , Yonghui Xu , Zhen Li , Mingzhe Zhang , Han Yu , Lizhen Cui , Xiangwei Zheng

Automatic grading of Gastric Intestinal Metaplasia (GIM) is valuable in assisting the diagnosis of early gastric cancer. Recently, prototypical networks are served as a effective method for medical image processing in few-shot scenarios. However, existing prototypical networks suffer from the following two limitations when applied to GIM grading: 1) Variable camera angles of gastric endoscopes result in diverse sampling granularities of GIM lesions, leading to a multitude of multiscale features. Fully supervised encoders struggle to learn robust multiscale features due to limited labeled endoscopic images and privacy concerns. 2) Class prototypes based on sample means ignore the latent class information of exceptional cases, resulting in one-sided inferences of category prototypes and decision boundaries. To address these challenges, we propose a Self-supervised Exceptional Prototypical Network (Swin-EPN) for few-shot grading of GIM. Specifically, three tailored pretext tasks are designed to jointly pretrain a swin transformer, which is integrated as the model’s embedding layer to learning robust multiscale features. We propose an exceptional prototype mining module that identifies exceptional prototypes by defining a prototype score for each sample and updating potential exceptional prototypes in an exceptional prototype bank. These exceptional prototypes are served as supplementary information to class prototypes, and are leveraged to guide the delineation of class decision boundaries. We validated Swin-EPN on a private GIM dataset from a local grade-A tertiary hospital in both 1-shot and 5-shot scenarios, achieving accuracy improvements of 6.12% and 5.61% respectively compared to state-of-the-art (SOTA) models.

胃肠化生（GIM）的自动分级对早期胃癌的诊断有重要价值。近年来，原型网络作为一种有效的方法被应用于医学图像的小镜头处理中。然而，现有的原型网络在应用于GIM分级时存在以下两方面的局限性：1)胃内窥镜的不同摄像角度导致GIM病变的采样粒度不同，导致大量的多尺度特征。由于有限的标记内窥镜图像和隐私问题，完全监督编码器难以学习鲁棒的多尺度特征。2)基于样本均值的类原型忽略了异常情况的潜在类信息，导致类原型和决策边界的片面推断。为了解决这些挑战，我们提出了一种自监督例外原型网络（swan - epn），用于GIM的几次分级。具体而言，设计了三个定制的借口任务来联合预训练旋转变压器，并将其集成为模型的嵌入层以学习鲁棒多尺度特征。我们提出了一个例外原型挖掘模块，该模块通过定义每个样本的原型得分和更新例外原型库中的潜在例外原型来识别例外原型。这些异常原型作为类原型的补充信息，并用于指导类决策边界的描述。我们在当地一家三级甲等医院的私人GIM数据集上验证了swwin - epn，在1发和5发两种情况下，与最先进的（SOTA）模型相比，准确率分别提高了6.12%和5.61%。

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引用次数: 0

Relation-aware pre-trained network with hierarchical aggregation mechanism for cold-start drug recommendation 基于层次聚合机制的关系感知预训练网络冷启动药物推荐

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-06-01 Epub Date: 2026-01-21 DOI: 10.1016/j.neunet.2026.108618

Xiaobo Li , Xiaodi Hou , Shilong Wang , Hongfei Lin , Yijia Zhang

Drug recommendation systems have garnered considerable interest in the healthcare, striving to offer precise and customized drug prescriptions that align with patients’ specific health needs. However, existing methods primarily focus on modeling temporal dependencies between visits for patients with multiple encounters, often neglecting the challenge of data sparsity in single-visit patients. To address above limitation, we propose a novel Relation-aware Pre-trained Network with hierarchical aggregation mechanism for drug recommendation (RPNet), which employs a pre-training and fine-tuning framework to enhance drug recommendation in cold-start scenario. Specifically, we introduce: 1) A code matching discrimination task during pre-training, designed to model the complex relationships between diagnosis and procedure entities. This task employs a mask-replace contrastive learning strategy, which pulls similar samples closer while pushing dissimilar ones apart, thereby capturing robust feature representations; 2) A hierarchical aggregation mechanism that enhances drug information integration by first selecting relevant visits based on rarity discrimination and then retrieving similar patients’ drug insights via similarity matching during fine-tuning. Extensive experiments on two real-world datasets demonstrate the superiority of the proposed RPNet, notably improving the F1 metric by 1.32% and 1.19%. The code of our model is available at https://github.com/Lxb0102/RPNet.

药物推荐系统已经在医疗保健领域获得了相当大的兴趣，努力提供精确和定制的药物处方，与患者的特定健康需求保持一致。然而，现有的方法主要侧重于对多次就诊患者就诊之间的时间依赖性建模，往往忽略了单次就诊患者的数据稀疏性的挑战。为了解决上述问题，我们提出了一种新的具有层次聚合机制的关系感知预训练药物推荐网络（RPNet），该网络采用预训练和微调框架来增强冷启动场景下的药物推荐。具体来说，我们介绍了：1)在预训练过程中，一个代码匹配判别任务，旨在对诊断和过程实体之间的复杂关系进行建模。该任务采用掩模替换对比学习策略，将相似的样本拉得更近，同时将不相似的样本分开，从而捕获鲁棒特征表示；2)层次聚合机制，首先基于稀缺性判别选择相关就诊，然后在微调过程中通过相似性匹配检索相似患者的药物见解，增强药物信息整合。在两个真实数据集上的大量实验证明了RPNet的优越性，显著提高了F1指标1.32%和1.19%。我们模型的代码可以在https://github.com/Lxb0102/RPNet上找到。

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引用次数: 0

Uncovering various neuronal responses in a fractional-order generalized HR system 揭示分数阶广义HR系统中的各种神经元反应。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-06-01 Epub Date: 2026-01-08 DOI: 10.1016/j.neunet.2026.108548

Krishnendu Bera , Chinmay Chakraborty , Eva Kaslik , Urszula Foryś , Sanjeev K. Sharma , Argha Mondal

This study investigates neuronal electrical activities in a fractional-order generalized Hindmarsh-Rose (HR) system and explores an extended model incorporating an induced electric field. Stability and bifurcation analyses examine the impact of external electrical stimulation on neuronal dynamics. The results show how electric field parameters, including amplitude and frequency, modulate neuronal excitability and stability. The H-R model is a mathematical representation that captures diverse neuronal activities, and the introduction of fractional-order derivatives allows us to explore non-local dynamics in greater depth. We analyze the effects of fractional-order derivatives on the system’s behavior, including the generation of action potential dynamics. We discuss some biophysical aspects of the different firing patterns that we encounter. In addition, the study employs both analytical and numerical methods to investigate the stability of bursting and spiking patterns, using linear stability analysis to examine the transitions between stable and unstable states. Simulations reveal significant memory effects even with a slight decrease in fractional order. This underscores the versatility of fractional-order models in bridging mathematical theory with biologically plausible phenomena. The findings of this study demonstrate the potential of fractional-order systems in capturing the intricacies of neuronal responses, highlighting the need for further exploration of these phenomena in excitable biophysical systems.

本文研究了分数阶广义Hindmarsh-Rose （HR）系统中的神经元电活动，并探索了一个包含感应电场的扩展模型。稳定性和分岔分析考察了外部电刺激对神经元动力学的影响。结果表明，电场参数（振幅和频率）对神经元的兴奋性和稳定性有调节作用。H-R模型是捕获不同神经元活动的数学表示，分数阶导数的引入使我们能够更深入地探索非局部动态。我们分析了分数阶导数对系统行为的影响，包括动作电位动力学的产生。我们讨论了我们遇到的不同放电模式的一些生物物理方面。此外，本研究采用解析和数值相结合的方法来研究爆裂和尖峰模式的稳定性，使用线性稳定性分析来研究稳定和不稳定状态之间的转换。模拟显示，即使分数阶略有下降，也会对记忆产生显著影响。这强调了分数阶模型在连接数学理论和生物学上似是而非的现象方面的多功能性。这项研究的发现证明了分数阶系统在捕捉神经元反应的复杂性方面的潜力，强调了在可兴奋生物物理系统中进一步探索这些现象的必要性。

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引用次数: 0

Efficient multi-agent communication via entity-aware causal network 基于实体感知因果网络的高效多智能体通信。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-06-01 Epub Date: 2026-01-09 DOI: 10.1016/j.neunet.2026.108538

Yifan Bo , Bowen Huang , Jinghan Feng , Shuo Zhang , Biao Leng

Communication is considered as a crucial approach for solving complicated multi-agent reinforcement learning (MARL) cooperative tasks. However, existing approaches rely on predefined agent orders and identifiers to learn targeted communication. The predefined approaches ignore the prior knowledge that the selection of communication targets is solely related to agents’ states rather than their orders or identifiers, which leads to poor scalability and inefficient sampling. To address these limitations, we introduce the Entity-Aware Causal (EAC) framework, which tackles MARL communication from an entity-centric perspective. The core idea is to enhance communication efficiency through entity-aware communication target selection and causal inference belief mechanism, we make three main contributions. Firstly, we design an entity-aware hypernetwork that identifies communication targets based on individual state information and employs a masked-attention mechanism to enable scalable and sparse communication topology. Secondly, we propose a causal inference beliefs mechanism to strengthen the belief of the communication between entities and reduce redundant message exchanges. Finally, our algorithm outperforms baseline multi-agent cooperative reinforcement learning algorithms across SMAC, SMAC_v2, GRF, and MPE benchmarks. We further demonstrate the robustness of the algorithm across various network topologies and sparsity levels.

通信被认为是解决复杂的多智能体强化学习（MARL）合作任务的关键方法。然而，现有的方法依赖于预定义的代理顺序和标识符来学习目标通信。预定义的方法忽略了通信目标的选择仅与代理的状态有关而与它们的顺序或标识符无关的先验知识，这导致了较差的可伸缩性和低效率的采样。为了解决这些限制，我们引入了实体感知因果（EAC）框架，该框架从实体中心的角度处理MARL通信。其核心思想是通过实体感知的通信目标选择和因果推理的信念机制来提高通信效率。首先，我们设计了一个基于个体状态信息识别通信目标的实体感知超网络，并采用掩码关注机制实现可扩展和稀疏的通信拓扑。其次，我们提出了一种因果推理信念机制，以加强实体之间通信的信念，减少冗余的信息交换。最后，我们的算法在SMAC、SMAC_v2、GRF和MPE基准测试中优于基线多智能体协作强化学习算法。我们进一步证明了该算法在各种网络拓扑和稀疏度级别上的鲁棒性。

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引用次数: 0

A novel cross-domain fault diagnosis method for multi-condition industrial processes based on meta-domain adaptation with progressive meta-learning 基于元域自适应和渐进式元学习的多工况工业过程跨域故障诊断方法。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-06-01 Epub Date: 2026-01-08 DOI: 10.1016/j.neunet.2026.108561

Xin Qin , Xuan Guo , Jie Dong , Kaixiang Peng

Complex industrial processes are characterized by high dynamics, diverse operating conditions, and strong inter-system coupling, often leading to reduced production efficiency and product quality fluctuations. Employing advanced fault diagnosis technologies has become an effective approach to support high-quality and efficient execution of industrial processes. However, the increasing prevalence of customized manufacturing has introduced substantial variability in working conditions, under which traditional fault diagnosis methods struggle to perform effectively. Each working condition can be abstracted as a domain. Therefore, employing domain adaptation techniques to achieve multi-condition fault diagnosis is one of the key approaches to addressing the above challenge. Based on the above observation, a novel neural network-based cross-domain fault diagnosis method for multi-condition industrial processes via meta-domain adaptation with progressive meta-learning is proposed. First, an adversarial dual-scale neural network is designed to address the challenge of feature alignment across multiple source domains, comprising a one-dimensional convolutional neural network feature extractor and a multi-layer perceptrons domain discriminator. A progressive adversarial strength adjustment strategy is proposed to better extract domain-invariant yet discriminative shared features, thereby enhancing domain generalization. Second, to tackle practical issues such as imbalanced condition distributions, limited sample availability, and intra-source heterogeneity, a meta-learning mechanism is employed to reduce internal distributional discrepancies within source domains. Additionally, multi-kernel maximum mean discrepancy is employed to explicitly align source and target features, facilitating robust generalization under substantial domain shifts. Finally, the constructed cross-domain feature extractor and fault classifier are used to achieve fault diagnosis in industrial processes. The proposed method is evaluated on the benchmark Tennessee Eastman process and a real hot strip mill process, demonstrating its effectiveness and superiority.

复杂的工业过程具有高动态性、多样化的操作条件和强系统间耦合的特点，往往导致生产效率降低和产品质量波动。采用先进的故障诊断技术已成为支持高质量和高效执行工业过程的有效途径。然而，定制制造的日益普及带来了工作条件的实质性变化，在这种情况下，传统的故障诊断方法难以有效地发挥作用。每个工况都可以抽象为一个域。因此，利用领域自适应技术实现多条件故障诊断是解决上述问题的关键途径之一。在此基础上，提出了一种基于神经网络的多工况工业过程跨域故障诊断方法，该方法基于元域自适应和渐进式元学习。首先，设计了一种对抗双尺度神经网络来解决跨多源域特征对齐的挑战，包括一维卷积神经网络特征提取器和多层感知器域鉴别器。提出了一种渐进式对抗强度调整策略，以更好地提取域不变但有区别的共享特征，从而提高域泛化能力。其次，为了解决条件分布不平衡、样本可用性有限和源内异质性等实际问题，采用元学习机制减少源域内部分布差异。此外，采用多核最大平均差异来显式对齐源和目标特征，便于在大量域偏移下进行鲁棒泛化。最后，利用构建的跨域特征提取器和故障分类器实现工业过程的故障诊断。通过田纳西州伊士曼工艺和热轧带钢实际工艺对该方法进行了评价，验证了该方法的有效性和优越性。

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引用次数: 0