IEEE transactions on neural networks and learning systems
Aug 31, 2022
For the considered reaction-diffusion inertial memristive neural networks (IMNNs), this article proposes a novel gain-scheduled generalized pinning control scheme, where three pinning control strategies are involved and 2 controller gains can be sche...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
Efficient neural architecture search (ENAS) achieves novel efficiency for learning architecture with high-performance via parameter sharing and reinforcement learning (RL). In the phase of architecture search, ENAS employs deep scalable architecture ...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
The need for medical image encryption is increasingly pronounced, for example, to safeguard the privacy of the patients' medical imaging data. In this article, a novel deep learning-based key generation network (DeepKeyGen) is proposed as a stream ci...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
This work proposes a decision tree (DT)-based method for initializing a dendritic neuron model (DNM). Neural networks become larger and larger, thus consuming more and more computing resources. This calls for a strong need to prune neurons that do no...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
Skeleton-based action recognition has been extensively studied, but it remains an unsolved problem because of the complex variations of skeleton joints in 3-D spatiotemporal space. To handle this issue, we propose a newly temporal-then-spatial recali...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
This article intends to address an online optimal adaptive regulation of nonlinear discrete-time systems in affine form and with partially uncertain dynamics using a multilayer neural network (MNN). The actor-critic framework estimates both the optim...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
The rapidly increasing volumes of data and the need for big data analytics have emphasized the need for algorithms that can accommodate incomplete or noisy data. The concept of recurrency is an important aspect of signal processing, providing greater...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
In a modern e-commerce recommender system, it is important to understand the relationships among products. Recognizing product relationships-such as complements or substitutes-accurately is an essential task for generating better recommendation resul...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
Graph-based learning in semisupervised models provides an effective tool for modeling big data sets in high-dimensional spaces. It has been useful for propagating a small set of initial labels to a large set of unlabeled data. Thus, it meets the requ...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping int...