AIMC Journal:
IEEE transactions on neural networks and learning systems

Showing 261 to 270 of 780 articles

Neural Adaptive Self-Triggered Control for Uncertain Nonlinear Systems With Input Hysteresis.

IEEE transactions on neural networks and learning systems Oct 27, 2022
The issue of neural adaptive self-triggered tracking control for uncertain nonlinear systems with input hysteresis is considered. Combining radial basis function neural networks (RBFNNs) and adaptive backstepping technique, an adaptive self-triggered...
Neural Networks, Computer Computer Simulation Nonlinear Dynamics Uncertainty
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Learning With Noisy Labels via Self-Reweighting From Class Centroids.

IEEE transactions on neural networks and learning systems Oct 27, 2022
Although deep neural networks have been proved effective in many applications, they are data hungry, and training deep models often requires laboriously labeled data. However, when labeled data contain erroneous labels, they often lead to model perfo...
Neural Networks, Computer
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Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information.

IEEE transactions on neural networks and learning systems Oct 27, 2022
Efficient processing of large-scale time-series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand-engineered feature extraction often involve huge computational costs with high dimensional dat...
Humans Neural Networks, Computer Machine Learning Electroencephalography Seizures Signal Processing, Computer-Assisted
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On the Sufficient Condition for Solving the Gap-Filling Problem Using Deep Convolutional Neural Networks.

IEEE transactions on neural networks and learning systems Oct 27, 2022
Deep convolutional neural networks (DCNNs) are routinely used for image segmentation of biomedical data sets to obtain quantitative measurements of cellular structures like tissues. These cellular structures often contain gaps in their boundaries, le...
Neural Networks, Computer Vision, Ocular
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A Comparative Study of Deep Neural Network-Aided Canonical Correlation Analysis-Based Process Monitoring and Fault Detection Methods.

IEEE transactions on neural networks and learning systems Oct 27, 2022
Multivariate analysis is an important kind of method in process monitoring and fault detection, in which the canonical correlation analysis (CCA) makes use of the correlation change between two groups of variables to distinguish the system status and...
Neural Networks, Computer Nonlinear Dynamics Canonical Correlation Analysis
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AutoMER: Spatiotemporal Neural Architecture Search for Microexpression Recognition.

IEEE transactions on neural networks and learning systems Oct 27, 2022
Facial microexpressions offer useful insights into subtle human emotions. This unpremeditated emotional leakage exhibits the true emotions of a person. However, the minute temporal changes in the video sequences are very difficult to model for accura...
Humans Neural Networks, Computer Algorithms Face
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Breaking Neural Reasoning Architectures With Metamorphic Relation-Based Adversarial Examples.

IEEE transactions on neural networks and learning systems Oct 27, 2022
The ability to read, reason, and infer lies at the heart of neural reasoning architectures. After all, the ability to perform logical reasoning over language remains a coveted goal of Artificial Intelligence. To this end, models such as the Turing-co...
Neural Networks, Computer Artificial Intelligence Machine Learning Computers Deep Learning
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Reinforcement Learning Control of Robotic Knee With Human-in-the-Loop by Flexible Policy Iteration.

IEEE transactions on neural networks and learning systems Oct 5, 2022
We are motivated by the real challenges presented in a human-robot system to develop new designs that are efficient at data level and with performance guarantees, such as stability and optimality at system level. Existing approximate/adaptive dynamic...
Humans Neural Networks, Computer Computer Simulation Policy Robotics Robotic Surgical Procedures
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Unified Analysis on the Global Dissipativity and Stability of Fractional-Order Multidimension-Valued Memristive Neural Networks With Time Delay.

IEEE transactions on neural networks and learning systems Oct 5, 2022
The unified criteria are analyzed on the global dissipativity and stability for the delayed fractional-order systems of multidimension-valued memristive neural networks (FSMVMNNs) in this article. First, based on the comprehensive knowledge about mul...
Neural Networks, Computer Algorithms Knowledge
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Maximum A Posteriori Approximation of Hidden Markov Models for Proportional Sequential Data Modeling With Simultaneous Feature Selection.

IEEE transactions on neural networks and learning systems Oct 5, 2022
One of the pillar generative machine learning approaches in time series data study and analysis is the hidden Markov model (HMM). Early research focused on the speech recognition application of the model with later expansion into numerous fields, inc...
Neural Networks, Computer Algorithms Markov Chains
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