IEEE transactions on neural networks and learning systems
Oct 5, 2022
Inferring brain-effective connectivity networks from neuroimaging data has become a very hot topic in neuroinformatics and bioinformatics. In recent years, the search methods based on Bayesian network score have been greatly developed and become an e...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
Face hallucination technologies have been widely developed during the past decades, among which the sparse manifold learning (SML)-based approaches have become the popular ones and achieved promising performance. However, these SML methods always fai...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
In classification, the use of 0-1 loss is preferable since the minimizer of 0-1 risk leads to the Bayes optimal classifier. However, due to the nonconvexity of 0-1 loss, this optimization problem is NP-hard. Therefore, many convex surrogate loss func...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
Design is an inseparable part of most scientific and engineering tasks, including real and simulation-based experimental design processes and parameter/hyperparameter tuning/optimization. Several model-based experimental design techniques have been d...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
This article addresses the spherical formation tracking control problem of nonlinear second-order vehicles moving in flowfields under both undirected networks and directed, strongly connected networks. Different from the previous adaptive estimate of...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
Current state-of-the-art visual recognition systems usually rely on the following pipeline: 1) pretraining a neural network on a large-scale data set (e.g., ImageNet) and 2) finetuning the network weights on a smaller, task-specific data set. Such a ...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
This article investigates the stability and synchronization of nonautonomous reaction-diffusion neural networks with general time-varying delays. Compared with the existing works concerning reaction-diffusion neural networks, the main innovation of t...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
This article develops an adaptive observation-based efficient reinforcement learning (RL) approach for systems with uncertain drift dynamics. A novel concurrent learning adaptive extended observer (CL-AEO) is first designed to jointly estimate the sy...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
With the aid of neural networks, this article develops two data-driven designs of fault detection (FD) for dynamic systems. The first neural network is constructed for generating residual signals in the so-called finite impulse response (FIR) filter-...
IEEE transactions on neural networks and learning systems
Oct 5, 2022
This article concerns the problems of synchronization in a fixed time or prespecified time for memristive complex-valued neural networks (MCVNNs), in which the state variables, activation functions, rates of neuron self-inhibition, neural connection ...