AIMC Journal:
IEEE transactions on neural networks and learning systems
Showing 381 to 390 of 780 articles
Recognizing Missing Electromyography Signal by Data Split Reorganization Strategy and Weight-Based Multiple Neural Network Voting Method.
IEEE transactions on neural networks and learning systems
May 2, 2022
Surface electromyography (sEMG) signals have been applied widely in prosthetic hand controlling. In the sEMG signal acquisition, wireless devices bring convenience, but also introduce signal missing due to interference or failure during data transmis...
Asymptotic Tracking Control for Uncertain MIMO Systems: A Biologically Inspired ESN Approach.
IEEE transactions on neural networks and learning systems
May 2, 2022
In this study, a biologically inspired echo state network (ESN)-based method is established for the asymptotic tracking control of a class of uncertain multi-input multi-output (MIMO) systems. By mimicking the characters of real biological systems, a...
Memory Recall: A Simple Neural Network Training Framework Against Catastrophic Forgetting.
IEEE transactions on neural networks and learning systems
May 2, 2022
It is widely acknowledged that biological intelligence is capable of learning continually without forgetting previously learned skills. Unfortunately, it has been widely observed that many artificial intelligence techniques, especially (deep) neural ...
Event-Driven Intrinsic Plasticity for Spiking Convolutional Neural Networks.
IEEE transactions on neural networks and learning systems
May 2, 2022
The biologically discovered intrinsic plasticity (IP) learning rule, which changes the intrinsic excitability of an individual neuron by adaptively turning the firing threshold, has been shown to be crucial for efficient information processing. Howev...
A Multiobjective Evolutionary Nonlinear Ensemble Learning With Evolutionary Feature Selection for Silicon Prediction in Blast Furnace.
IEEE transactions on neural networks and learning systems
May 2, 2022
In the blast furnace ironmaking process, accurate prediction of silicon content in molten iron is of great significance for maintaining stable furnace conditions, improving hot metal quality, and reducing energy consumption. However, most of the curr...
StructADMM: Achieving Ultrahigh Efficiency in Structured Pruning for DNNs.
IEEE transactions on neural networks and learning systems
May 2, 2022
Weight pruning methods of deep neural networks (DNNs) have been demonstrated to achieve a good model pruning rate without loss of accuracy, thereby alleviating the significant computation/storage requirements of large-scale DNNs. Structured weight pr...
A Neuromorphic CMOS Circuit With Self-Repairing Capability.
IEEE transactions on neural networks and learning systems
May 2, 2022
Neurophysiological observations confirm that the brain not only is able to detect the impaired synapses (in brain damage) but also it is relatively capable of repairing faulty synapses. It has been shown that retrograde signaling by astrocytes leads ...
Robust Facial Landmark Detection by Multiorder Multiconstraint Deep Networks.
IEEE transactions on neural networks and learning systems
May 2, 2022
Recently, heatmap regression has been widely explored in facial landmark detection and obtained remarkable performance. However, most of the existing heatmap regression-based facial landmark detection methods neglect to explore the high-order feature...
Memristive Circuit Implementation of a Self-Repairing Network Based on Biological Astrocytes in Robot Application.
IEEE transactions on neural networks and learning systems
May 2, 2022
A large number of studies have shown that astrocytes can be combined with the presynaptic terminals and postsynaptic spines of neurons to constitute a triple synapse via an endocannabinoid retrograde messenger to achieve a self-repair ability in the ...
Contrastive Adversarial Domain Adaptation Networks for Speaker Recognition.
IEEE transactions on neural networks and learning systems
May 2, 2022
Domain adaptation aims to reduce the mismatch between the source and target domains. A domain adversarial network (DAN) has been recently proposed to incorporate adversarial learning into deep neural networks to create a domain-invariant space. Howev...
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