IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第10页

Discriminative Multi-View Fusion via Adaptive Regression 通过自适应回归实现判别式多视图融合

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-21 DOI: 10.1109/TETCI.2024.3375342

Chenglong Zhang;Xinjie Zhu;Zidong Wang;Yan Zhong;Weiguo Sheng;Weiping Ding;Bingbing Jiang

{"title":"Discriminative Multi-View Fusion via Adaptive Regression","authors":"Chenglong Zhang;Xinjie Zhu;Zidong Wang;Yan Zhong;Weiguo Sheng;Weiping Ding;Bingbing Jiang","doi":"10.1109/TETCI.2024.3375342","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3375342","url":null,"abstract":"Data fusion has become an important task in multi-view learning. Previous methods suffer from the insufficient data fusion due to the following issues: (i) Several methods ignore the correlation and distinction among views and directly concatenate the features from different views; (ii) They involve intractable parameters to balance different views, degenerating the applicability of models; (iii) A fixed label matrix is used to guide feature fusion, overlooking the distances between different classes (i.e., inter-class distance) or within the same class (i.e., intra-class compactness). To overcome these problems, a novel fusion model is proposed to discriminate different views and samples in an adaptive manner, so as to effectively reduce the adverse impacts of low-quality views and outliers. In contrast to existing methods, a flexible regression target is designed to take full advantage of the label information of data, such that both the inter-class distance and the intra-class compactness are preserved. Benefiting from this, a compact and discriminative representation of multiple views is learned to maintain the consistent and complementary information of diverse views. Extensive experiments validate the effectiveness and the superiority of our proposed model.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"3821-3833"},"PeriodicalIF":5.3,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Convolutional Fuzzy Neural Networks With Random Weights for Image Classification 用于图像分类的具有随机权重的卷积模糊神经网络

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-20 DOI: 10.1109/TETCI.2024.3375019

Yifan Wang;Hisao Ishibuchi;Witold Pedrycz;Jihua Zhu;Xiangyong Cao;Jun Wang

{"title":"Convolutional Fuzzy Neural Networks With Random Weights for Image Classification","authors":"Yifan Wang;Hisao Ishibuchi;Witold Pedrycz;Jihua Zhu;Xiangyong Cao;Jun Wang","doi":"10.1109/TETCI.2024.3375019","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3375019","url":null,"abstract":"Deep fuzzy neural networks have established a fundamental connection between fuzzy systems and deep learning networks, serving as a crucial bridge between two research fields in computational intelligence. These hybrid networks have powerful learning capability stemming from deep neural networks while leveraging the advantages of fuzzy systems, such as robustness. Due to these benefits, deep fuzzy neural networks have recently been an emerging topic in computational intelligence. With the help of deep learning, fuzzy systems have achieved great performance on the classification task. Although fuzzy systems have been extensively investigated, they still struggle in terms of image classification. In this paper, we propose a convolutional fuzzy neural network that combines improved convolutional neural networks with a fuzzy-set-based fusion technique. Different from convolutional neural networks, filters are randomly generated in convolutional layers in our model. This operation not only leads to the fast learning of the model but also avoids some notorious problems of gradient descent procedures in conventional deep learning methods. Extensive experiments demonstrate that the proposed approach is competitive with state-of-the-art fuzzy models and deep learning models. Compared to classical deep models that require massive training data, the proposed approach works well on small datasets.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3279-3293"},"PeriodicalIF":5.3,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Jointly Optimized Classifiers for Few-Shot Class-Incremental Learning 联合优化分类器，实现少镜头分类增量学习

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-20 DOI: 10.1109/TETCI.2024.3375509

Sichao Fu;Qinmu Peng;Xiaorui Wang;Yang He;Wenhao Qiu;Bin Zou;Duanquan Xu;Xiao-Yuan Jing;Xinge You

{"title":"Jointly Optimized Classifiers for Few-Shot Class-Incremental Learning","authors":"Sichao Fu;Qinmu Peng;Xiaorui Wang;Yang He;Wenhao Qiu;Bin Zou;Duanquan Xu;Xiao-Yuan Jing;Xinge You","doi":"10.1109/TETCI.2024.3375509","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3375509","url":null,"abstract":"Few-shot class-incremental learning (FSCIL) has recently aroused widespread research interest, which aims to continually learn new class knowledge from a few labeled samples without ignoring the previous concept. One typical method is graph-based FSCIL (GFSCIL), which tends to design more complex message-passing schemes to make the classifiers' decision boundary clearer. However, it would result in poor extrapolating ability because no effort was paid to consider the effectiveness of the trained feature backbone and the learned topology structure. In this paper, we propose a simple and effective GFSCIL framework to solve the above-mentioned problem, termed Jointly Optimized Classifiers (JOC). Specifically, a simple multi-task training module incorporates both classification and auxiliary task loss to jointly supervise the feature backbone trained on the base classes. By doing so, our proposed JOC can effectively improve the robustness of the trained feature backbone, without the utilization of extra datasets or complex feature backbones. To avoid new class overfitting and old class knowledge forgetting issues of the trained feature backbone, the decouple learning strategy is adopted to fix the feature backbone parameters and only optimize the classifier parameters for the new classes. Finally, a spatial-channel graph attention network is designed to simultaneously preserve the global and local similar relationships between all classes for improving the generalization performance of classifiers. To demonstrate the effectiveness of the proposed method, extensive experiments were conducted on three popular datasets. Experimental results show that our proposed JOC outperforms many existing state-of-the-art FSCIL.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3316-3326"},"PeriodicalIF":5.3,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

MFLink: User Identity Linkage Across Online Social Networks via Multimodal Fusion and Adversarial Learning MFLink：通过多模态融合和对抗学习实现在线社交网络中的用户身份链接

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-20 DOI: 10.1109/TETCI.2024.3372374

Shudong Li;Danna Lu;Qing Li;Xiaobo Wu;Shumei Li;Zhen Wang

{"title":"MFLink: User Identity Linkage Across Online Social Networks via Multimodal Fusion and Adversarial Learning","authors":"Shudong Li;Danna Lu;Qing Li;Xiaobo Wu;Shumei Li;Zhen Wang","doi":"10.1109/TETCI.2024.3372374","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3372374","url":null,"abstract":"As an essential step in the online social network research, user identity linkage aims to identify different accounts belonging to the same natural person. Many existing methods rely on single-modal approaches, which cannot provide a comprehensive user description. Some methods also fail to address the semantic gaps in data across different social platforms. To concurrently address these issues, this paper explores user identity linkage across online social networks by leveraging three types of modal information of users: attributes, post content, and social relationships. We propose a user identity linkage scheme named MFLink based on multimodal fusion, which has three components: Feature Extraction, Multimodal Fusion, and Adversarial Learning. In the Feature Extraction, MFLink utilizes feature embedding methods to transfer the user attribute and post content into intermediate representations. To achieve optimal fusion of information from these three modalities, MFLink integrates each modality with the assistance of graph neural networks and an attention mechanism within the Multimodal Fusion. Finally, MFLink employs adversarial learning to enhance the similarity of representations for the same individual across various platforms. The experiment results on the TWFQ dataset indicate that MFLink outperforms the advanced approaches in fusing information of modalities and addressing the data semantic gaps across online social networks.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3716-3725"},"PeriodicalIF":5.3,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Synchronized Video and EEG Based Childhood Epilepsy Seizure Detection 基于视频和脑电图的儿童癫痫发作同步检测

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-19 DOI: 10.1109/TETCI.2024.3372387

Jiuwen Cao;Yuan Fang;Xiaonan Cui;Runze Zheng;Tiejia Jiang;Feng Gao

{"title":"Synchronized Video and EEG Based Childhood Epilepsy Seizure Detection","authors":"Jiuwen Cao;Yuan Fang;Xiaonan Cui;Runze Zheng;Tiejia Jiang;Feng Gao","doi":"10.1109/TETCI.2024.3372387","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3372387","url":null,"abstract":"Childhood epilepsy seriously affects the nervous system development of children. Electroencephalogram (EEG) based epilepsy analysis is common in the past, but the inconvenient acquisition of EEG is the main challenge. In this paper, we firstly explored seizure detection performance of multi-modal synchronized video and EEG method. Further, we explore seizure detection only using video modal data. A novel childhood multi-modal epilepsy seizure detection algorithm using YOLO\u0000<inline-formula><tex-math>$_{v3}$</tex-math></inline-formula>\u0000 for object detection, hybrid discriminate video and EEG feature representation is developed in the paper. After screening out interferences in video sequence by YOLO\u0000<inline-formula><tex-math>$_{v3}$</tex-math></inline-formula>\u0000, the space-time interest points (STIPs) are extracted to characterize the body movement. The space-time interest points (STIPs) are extracted to characterize the body movement. Then, 4 popular features, Histogram of Oriented Gradient (HOG), Histograms of Oriented Optical Flow (HOF), Local Binary Pattern (LBP), and Motion Boundary Histogram (MBH) around STIPs are extracted. The Histograms of Word Frequency (HWF) features derived from a bag of words (BOW) model on HOG, HOF, LBP and MBH are developed for video representation. Meanwhile, the MelFrequency Cepstral Coefficients (MFCC) and Linear Predictive Cepstral Coefficients (LPCC) are extracted for EEG characterization. Multi-modal data of 13 childhood epilepsy patients from the Children's Hospital, Zhejiang University School of Medicine (CHZU) are studied. The fused EEG+Video feature based method could achieve an overall accuracy of 98.33%. Moreover, only using video feature, the method can achieve an overall accuracy of 93.30%.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"3742-3753"},"PeriodicalIF":5.3,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Game-Theoretic Expert Importance Evaluation Model Guided by Cooperation Effects for Social Network Group Decision Making 社会网络群体决策中以合作效应为指导的博弈论专家重要性评估模型

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-19 DOI: 10.1109/TETCI.2024.3372410

Zeyi Liu;Tao Wen;Yong Deng;Hamido Fujita

{"title":"Game-Theoretic Expert Importance Evaluation Model Guided by Cooperation Effects for Social Network Group Decision Making","authors":"Zeyi Liu;Tao Wen;Yong Deng;Hamido Fujita","doi":"10.1109/TETCI.2024.3372410","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3372410","url":null,"abstract":"The evaluation of expert importance degree for solving group decision-making problems (GDM) is meaningful, especially for social network GDM cases. Conventionally, the importance of experts in existing GDM models is assumed to be isolated. Nevertheless, in real-life scenarios, the internal components of expert systems should be mutually influential. In this study, a novel game-theoretic expert importance evaluation model guided by cooperation effects is proposed. First, the framework of non-additive fuzzy measure values is utilized to obtain the initial opinions of all experts. An interaction indicator is then exploited to represent peer interaction effort (PIE). With the log-sigmoid transition technique, individual social cooperation networks (ISCNs) are then constructed. With the advanced aggregation operator, the global social cooperation network (GSCN) of the corresponding expert collection can be generated. Eventually, a modified gravity model is designed to evaluate the degree of importance for the experts. Several experiments are conducted to demonstrate the effectiveness of the proposed method. The results show that the influence of cooperation effects can reasonably be considered in the expert importance evaluation procedure, which is beneficial to real-life scenarios. Additional comparisons and related discussions are also provided.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"2749-2761"},"PeriodicalIF":5.3,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Global Convolutional Self-Action Module for Fast Brain Tumor Image Segmentation 用于快速脑肿瘤图像分割的全局卷积自作用模块

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-19 DOI: 10.1109/TETCI.2024.3375075

Wei-An Yang;Devin Lautan;Tong-Wei Weng;Wan-Chun Lin;Yamin Kao;Chien-Chang Chen

{"title":"Global Convolutional Self-Action Module for Fast Brain Tumor Image Segmentation","authors":"Wei-An Yang;Devin Lautan;Tong-Wei Weng;Wan-Chun Lin;Yamin Kao;Chien-Chang Chen","doi":"10.1109/TETCI.2024.3375075","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3375075","url":null,"abstract":"Integrating frameworks of Fermi normalization and fast data density functional transform (fDDFT), we established a new global convolutional self-action module to reduce the computational complexity in modern deep convolutional neural networks (CNNs). The Fermi normalization conflates mathematical properties of sigmoid function and z-score normalization with high efficiency. Global convolutional kernels embedded in the fDDFT simultaneously extract global features from whole input images through long-range dependency. The fDDFT endows the transformed images with a smoothness property, so the images can be substantially down-sampled before the global convolutions and then resized back to the original dimensions without losing accuracy. To inspect the feasibility of the synergy of Fermi normalization and fDDFT and the combinational effect with modern CNNs, we applied the dimension-fusion U-Net as a backbone and utilized the datasets from BraTS 2020. Experimental results exhibited that the model embedded with the module saved 57%–60% computational costs and raised 50%–53% inferencing speeds compared to the naïve D-UNet model. Furthermore, the module enhanced the accuracy of brain tumor image segmentation. The dice scores of the work are 0.9221 for whole tumors, 0.8760 for tumor cores, 0.8659 for enhancing tumors, and 0.8362 for peritumoral edema. These results exhibit comparable performance to the winner of BraTS 2020. Our results also validate that image inputs processed by the module provide aligned and unified bases, establishing a specific space with optimized feature map combinations to reduce computational complexity efficiently. The module significantly boosted the performance of training and inferencing without losing model accuracy.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"3848-3859"},"PeriodicalIF":5.3,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Deep Regression Modeling for Imbalanced and Incomplete Time-Series Data 针对不平衡和不完整时间序列数据的深度回归建模

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-19 DOI: 10.1109/TETCI.2024.3372435

Murtadha D. Hssayeni;Behnaz Ghoraani

{"title":"Deep Regression Modeling for Imbalanced and Incomplete Time-Series Data","authors":"Murtadha D. Hssayeni;Behnaz Ghoraani","doi":"10.1109/TETCI.2024.3372435","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3372435","url":null,"abstract":"During the collection of time-series data, many reasons lead to imbalanced and incomplete datasets. Consequently, it becomes challenging to develop deep convolutional models without suffering from overfitting. Our objective in this paper was to investigate an emerging but rather underutilized framework of Conditional Generative Adversarial Networks (cGANs) for improving deep regression models for time-series data with an imbalanced and incomplete distribution. First, we investigated the potential of using a vanilla cGAN as a data imputation to improve the generalizability of the developed models to unseen data in such datasets. Next, we proposed a modified cGAN architecture with improved extrapolation and generalizability of the regression models. Our investigations used an imbalanced synthetic non-stationary dataset, a real-world dataset in Parkinson's disease (PD) application domain, and one publicly-available dataset for Negative Affect (NA) estimation. We found that vanilla cGAN failed to generate realistic time-series data due to severe mode collapse, limiting its application as a data imputation for imbalanced and incomplete data. Importantly, the proposed cGAN framework significantly improved extrapolation and generalizability for the prediction of regression scores with an average improvement of 56%, 34%, and 18%, respectively, in mean absolute error for the synthetic, PD, and NA datasets when compared with traditional Convolutional Neural Networks. The codes are publicly available on Github.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"3767-3778"},"PeriodicalIF":5.3,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Graph Structure Enhanced Pre-Training Language Model for Knowledge Graph Completion 用于知识图谱补全的图结构增强型预培训语言模型

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-19 DOI: 10.1109/TETCI.2024.3372442

Huashi Zhu;Dexuan Xu;Yu Huang;Zhi Jin;Weiping Ding;Jiahui Tong;Guoshuang Chong

{"title":"Graph Structure Enhanced Pre-Training Language Model for Knowledge Graph Completion","authors":"Huashi Zhu;Dexuan Xu;Yu Huang;Zhi Jin;Weiping Ding;Jiahui Tong;Guoshuang Chong","doi":"10.1109/TETCI.2024.3372442","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3372442","url":null,"abstract":"A vast amount of textual and structural information is required for knowledge graph construction and its downstream tasks. However, most of the current knowledge graphs are incomplete due to the difficulty of knowledge acquisition and integration. Knowledge Graph Completion (KGC) is used to predict missing connections. In previous studies, textual information and graph structural information are utilized independently, without an effective method for fusing these two types of information. In this paper, we propose a graph structure enhanced pre-training language model for knowledge graph completion. Firstly, we design a graph sampling algorithm and a Graph2Seq module for constructing sub-graphs and their corresponding contexts to support large-scale knowledge graph learning and parallel training. It is also the basis for fusing textual data and graph structure. Next, two pre-training tasks based on masked modeling are designed for capturing accurate entity-level and relation-level information. Furthermore, this paper proposes a novel asymmetric Encoder-Decoder architecture to restore masked components, where the encoder is a Pre-trained Language Model (PLM) and the decoder is a multi-relational Graph Neural Network (GNN). The purpose of the architecture is to integrate textual information effectively with graph structural information. Finally, the model is fine-tuned for KGC tasks on two widely used public datasets. The experiments show that the model achieves excellent performance and outperforms baselines in most metrics, which demonstrate the effectiveness of our approach by fusing the structure and semantic information to knowledge graph.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"2697-2708"},"PeriodicalIF":5.3,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Survey of Neurodynamic Optimization 神经动力优化调查

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-19 DOI: 10.1109/TETCI.2024.3369667

Youshen Xia;Qingshan Liu;Jun Wang;Andrzej Cichocki

引用次数: 0