IEEE transactions on neural networks and learning systems
Jan 4, 2021
Learning vector quantization (LVQ) is a simple and efficient classification method, enjoying great popularity. However, in many classification scenarios, such as electroencephalogram (EEG) classification, the input features are represented by symmetr...
IEEE transactions on neural networks and learning systems
Jan 4, 2021
This article addresses the multistability and attraction of fractional-order neural networks (FONNs) with unbounded time-varying delays. Several sufficient conditions are given to ensure the coexistence of equilibrium points (EPs) of FONNs with conca...
IEEE transactions on neural networks and learning systems
Jan 4, 2021
This article solves the exponential synchronization issue of memristor-based complex-valued neural networks (MCVNNs) with time-varying uncertainties via feedback control. Compared with the traditional control methods, a more practical and general con...
IEEE transactions on neural networks and learning systems
Jan 4, 2021
This article presents new theoretical results on global exponential synchronization of nonlinear coupled delayed memristive neural networks with reaction-diffusion terms and Dirichlet boundary conditions. First, a state-dependent memristive neural ne...
IEEE transactions on neural networks and learning systems
Jan 4, 2021
The state-of-the-art multitask multiview (MTMV) learning tackles a scenario where multiple tasks are related to each other via multiple shared feature views. However, in many real-world scenarios where a sequence of the multiview task comes, the high...
IEEE transactions on neural networks and learning systems
Jan 4, 2021
Despite their accuracy, neural network-based classifiers are still prone to manipulation through adversarial perturbations. These perturbations are designed to be misclassified by the neural network while being perceptually identical to some valid in...
IEEE transactions on neural networks and learning systems
Jan 4, 2021
With the rapid development of sensor and information technology, now multisensor data relating to the system degradation process are readily available for condition monitoring and remaining useful life (RUL) prediction. The traditional data fusion an...
IEEE transactions on neural networks and learning systems
Jan 4, 2021
This article presents a two-timescale duplex neurodynamic approach to mixed-integer optimization, based on a biconvex optimization problem reformulation with additional bilinear equality or inequality constraints. The proposed approach employs two re...
IEEE transactions on neural networks and learning systems
Jan 4, 2021
Open-domain dialog generation, which is a crucial component of artificial intelligence, is an essential and challenging problem. In this article, we present a personalized dialog system, which leverages the advantages of multitask learning and reinfo...
IEEE transactions on neural networks and learning systems
Jan 4, 2021
Deep convolutional neural networks have been widely used in numerous applications, but their demanding storage and computational resource requirements prevent their applications on mobile devices. Knowledge distillation aims to optimize a portable st...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.