IEEE transactions on neural networks and learning systems
Jul 6, 2023
Accurate estimation of reservoir parameters (e.g., permeability and porosity) helps to understand the movement of underground fluids. However, reservoir parameters are usually expensive and time-consuming to obtain through petrophysical experiments o...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
In this article, an unsupervised domain adaptation strategy has been investigated using a deep Siamese neural network in scene-level land cover classification using remotely sensed images. At the onset, the soft class label and probability scores of ...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Seeking good correspondences between two images is a fundamental and challenging problem in the remote sensing (RS) community, and it is a critical prerequisite in a wide range of feature-based visual tasks. In this article, we propose a flexible and...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
This article develops two novel output feedback (OPFB) Q -learning algorithms, on-policy Q -learning and off-policy Q -learning, to solve H static OPFB control problem of linear discrete-time (DT) systems. The primary contribution of the proposed alg...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
In stochastic optimization problems where only noisy zeroth-order (ZO) oracles are available, the Kiefer-Wolfowitz algorithm and its randomized counterparts are widely used as gradient estimators. Existing algorithms generate the random perturbations...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
This article investigates the local stability and local convergence of a class of neural network (NN) controllers with error integrals as inputs for reference tracking. It is formally proved that if the input of the NN controller consists exclusively...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
This article focuses on the finite-time and fixed-time synchronization of a class of coupled discontinuous neural networks, which can be viewed as a combination of the Hindmarsh-Rose model and the Kuramoto model. To this end, under the framework of F...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Direct-optimization-based dictionary learning has attracted increasing attention for improving computational efficiency. However, the existing direct optimization scheme can only be applied to limited dictionary learning problems, and it remains an o...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Network embedding is to learn low-dimensional representations of nodes while preserving necessary information for network analysis tasks. Though representations preserving both structure and attribute features have achieved in many real-world applica...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Training deep neural networks (DNNs) rested heavily on efficient local solvers. Due to their local property, local solvers are sensitive to initialization and hyperparameters. In this article, a systematical method for finding multiple high-quality l...