Ieee Transactions on Neural Networks and Learning Systems
Overview
The IEEE Transactions on Neural Networks and Learning Systems is a prestigious journal that focuses on the latest advancements in the field of neural networks and learning systems. It covers a wide range of topics including computational models, algorithms, and applications related to neural networks, deep learning, machine learning, and cognitive systems. The journal provides a platform for researchers, engineers, and practitioners to share their innovative research findings and contribute to the development of intelligent systems and technologies.
Details
Details
Abbr.
IEEE Trans Neural Netw Learn Syst
Start
2012
End
Continuing
Frequency
Monthly
p-ISSN
2162-237X
e-ISSN
2162-2388
Country
United States
Language
English
Specialties
Biomedical Engineering
Medical Informatics
Medical Informatics
Metrics
Metrics
h-index / Ranks: 361
251
SJR / Ranks: 356
4170
CiteScore / Ranks: 287
21.90
JIF / Ranks: 433
10.4
Recent Articles
1.
Li W, Wu X, Fan S, Wei S, Gowing G
IEEE Trans Neural Netw Learn Syst
. 2025 Mar;
PP.
PMID: 40085466
The key requirement for underwater image enhancement (UIE) is to overcome the unpredictable color degradation caused by the underwater environment and light attenuation, while addressing issues, such as color distortion,...
2.
Li D, Huang S, Xie L, Wang Z, Xu J
IEEE Trans Neural Netw Learn Syst
. 2025 Mar;
PP.
PMID: 40085465
Considerable interindividual variability exists in electroencephalogram (EEG) signals, resulting in challenges for subject-independent emotion recognition tasks. Current research in cross-subject EEG emotion recognition has been insufficient in uncovering the shared...
3.
Zhang C, Pan W, Santina C
IEEE Trans Neural Netw Learn Syst
. 2025 Mar;
PP.
PMID: 40085464
Motor imagery (MI), an important category in electroencephalogram (EEG) research, often intersects with scenarios demanding low energy consumption, such as portable medical devices and isolated environment operations. Traditional deep learning...
4.
Zhang M, Luo X, Wu J, Belatreche A, Cai S, Yang Y, et al.
IEEE Trans Neural Netw Learn Syst
. 2025 Mar;
PP.
PMID: 40085463
The brain is able to acquire and store memories of everyday experiences in real-time. It can also selectively forget information to facilitate memory updating. However, our understanding of the underlying...
5.
Shi B, Li D
IEEE Trans Neural Netw Learn Syst
. 2025 Mar;
PP.
PMID: 40085462
Compressive imaging (CI) aims to recover the underlying image from the under-sampled observations. Recently, deep unfolded CI (DUCI) algorithms, which unfold the iterative algorithms into deep neural networks (DNNs), have...
6.
Cui Q, Feng G, Xu X
IEEE Trans Neural Netw Learn Syst
. 2025 Mar;
PP.
PMID: 40085461
This brief presents a novel optimal control (OC) approach based on Q-learning to address robust control challenges for uncertain nonlinear systems subject to mismatched perturbations. Unlike conventional methodologies that solve...
7.
Huang S, Feng W, Tang C, He Z, Yu C, Lv J
IEEE Trans Neural Netw Learn Syst
. 2025 Mar;
PP.
PMID: 40085460
Many problems in science and engineering can be mathematically modeled using partial differential equations (PDEs), which are essential for fields like computational fluid dynamics (CFD), molecular dynamics, and dynamical systems....
8.
Yan W, Zhu J, Zhou Y, Chen J, Cheng H, Yue K, et al.
IEEE Trans Neural Netw Learn Syst
. 2025 Mar;
PP.
PMID: 40080381
Driven by the complementarity and consistency inherent in multiview data, multiview clustering (MVC) has garnered widespread attention in various domains. Real-world data often encounters the issue of missing information, leading...
9.
Hu W, Wu J, Qian Q
IEEE Trans Neural Netw Learn Syst
. 2025 Mar;
PP.
PMID: 40080380
In this article, we propose a new graph neural network (GNN) explainability model, CiRLExplainer, which elucidates GNN predictions from a causal attribution perspective. Initially, a causal graph is constructed to...
10.
Qian C, Lao X, Li C
IEEE Trans Neural Netw Learn Syst
. 2025 Mar;
PP.
PMID: 40067735
Anomaly localization, which involves localizing anomalous regions within images, is a significant industrial task. Reconstruction-based methods are widely adopted for anomaly localization because of their low complexity and high interpretability....