IEEE transactions on neural networks and learning systems
Jul 6, 2023
Fiber-optic distributed acoustic sensing (DAS) is an emerging technology for vibration measurements with numerous applications in seismic signal analysis, including microseismicity detection, ambient noise tomography, earthquake source characterizati...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Sea subsurface temperature, an essential component of aquatic wildlife, underwater dynamics, and heat transfer with the sea surface, is affected by global warming in climate change. Existing research is commonly based on either physics-based numerica...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
This article investigates the tracking control problem for a class of nonlinear multi-input-multi-output (MIMO) uncertain singularly perturbed systems (SPSs) with full-state constraints. The underlying issues become more challenging because two-time-...
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 investigates the issue of synchronization for a type of uncertain coupled neural networks (CNNs) involving time-varying delay with unmeasured or unknown bound by delayed impulsive control with distributed delay. A new Halanay-like delaye...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Deep reinforcement learning (DRL) is a machine learning method based on rewards, which can be extended to solve some complex and realistic decision-making problems. Autonomous driving needs to deal with a variety of complex and changeable traffic sce...
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 investigates the problem of relaxed exponential stabilization for coupled memristive neural networks (CMNNs) with connection fault and multiple delays via an optimized elastic event-triggered mechanism (OEEM). The connection fault of the...