AIMC Journal:
IEEE transactions on neural networks and learning systems

Showing 221 to 230 of 780 articles

A Novel Time-Series Memory Auto-Encoder With Sequentially Updated Reconstructions for Remaining Useful Life Prediction.

IEEE transactions on neural networks and learning systems Nov 30, 2022
One of the significant tasks in remaining useful life (RUL) prediction is to find a good health indicator (HI) that can effectively represent the degradation process of a system. However, it is difficult for traditional data-driven methods to constru...
Neural Networks, Computer Time Factors
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Diagonal Recurrent Neural Network-Based Hysteresis Modeling.

IEEE transactions on neural networks and learning systems Nov 30, 2022
The Preisach model and the neural networks are two of the most popular strategies to model hysteresis. In this article, we first mathematically prove that the rate-independent Preisach model is actually a diagonal recurrent neural network (dRNN) with...
Neural Networks, Computer Neurons
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Subarchitecture Ensemble Pruning in Neural Architecture Search.

IEEE transactions on neural networks and learning systems Nov 30, 2022
Neural architecture search (NAS) is gaining more and more attention in recent years because of its flexibility and remarkable capability to reduce the burden of neural network design. To achieve better performance, however, the searching process usua...
Neural Networks, Computer Algorithms
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Incremental Deep Neural Network Learning Using Classification Confidence Thresholding.

IEEE transactions on neural networks and learning systems Nov 30, 2022
Most modern neural networks for classification fail to take into account the concept of the unknown. Trained neural networks are usually tested in an unrealistic scenario with only examples from a closed set of known classes. In an attempt to develop...
Neural Networks, Computer Machine Learning Learning
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Achieving Online Regression Performance of LSTMs With Simple RNNs.

IEEE transactions on neural networks and learning systems Nov 30, 2022
Recurrent neural networks (RNNs) are widely used for online regression due to their ability to generalize nonlinear temporal dependencies. As an RNN model, long short-term memory networks (LSTMs) are commonly preferred in practice, as these networks ...
Neural Networks, Computer Algorithms Learning
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Position-Aware Participation-Contributed Temporal Dynamic Model for Group Activity Recognition.

IEEE transactions on neural networks and learning systems Nov 30, 2022
Group activity recognition (GAR) aiming at understanding the behavior of a group of people in a video clip has received increasing attention recently. Nevertheless, most of the existing solutions ignore that not all the persons contribute to the grou...
Humans Neural Networks, Computer Attention Motion Perception Recognition, Psychology
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Inverse-Based Approach to Explaining and Visualizing Convolutional Neural Networks.

IEEE transactions on neural networks and learning systems Nov 30, 2022
This article presents a new method for understanding and visualizing convolutional neural networks (CNNs). Most existing approaches to this problem focus on a global score and evaluate the pixelwise contribution of inputs to the score. The analysis o...
Humans Neural Networks, Computer
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Vision-Based Topological Mapping and Navigation With Self-Organizing Neural Networks.

IEEE transactions on neural networks and learning systems Nov 30, 2022
Spatial mapping and navigation are critical cognitive functions of autonomous agents, enabling one to learn an internal representation of an environment and move through space with real-time sensory inputs, such as visual observations. Existing model...
Humans Neural Networks, Computer Learning Cognition Knowledge Movement Spatial Navigation
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SeReNe: Sensitivity-Based Regularization of Neurons for Structured Sparsity in Neural Networks.

IEEE transactions on neural networks and learning systems Nov 30, 2022
Deep neural networks include millions of learnable parameters, making their deployment over resource-constrained devices problematic. Sensitivity-based regularization of neurons (SeReNe) is a method for learning sparse topologies with a structure, ex...
Neural Networks, Computer Algorithms Neurons Data Compression
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Tuning Convolutional Spiking Neural Network With Biologically Plausible Reward Propagation.

IEEE transactions on neural networks and learning systems Nov 30, 2022
Spiking neural networks (SNNs) contain more biologically realistic structures and biologically inspired learning principles than those in standard artificial neural networks (ANNs). SNNs are considered the third generation of ANNs, powerful on the ro...
Neural Networks, Computer Algorithms Models, Neurological Neurons Reward
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