AIMC Journal:
IEEE transactions on neural networks and learning systems
Showing 461 to 470 of 780 articles
Numerical Spiking Neural P Systems.
IEEE transactions on neural networks and learning systems
Jun 2, 2021
Spiking neural P (SN P) systems are a class of discrete neuron-inspired computation models, where information is encoded by the numbers of spikes in neurons and the timing of spikes. However, due to the discontinuous nature of the integrate-and-fire ...
Convolutional Sparse Support Estimator-Based COVID-19 Recognition From X-Ray Images.
IEEE transactions on neural networks and learning systems
May 3, 2021
Coronavirus disease (COVID-19) has been the main agenda of the whole world ever since it came into sight. X-ray imaging is a common and easily accessible tool that has great potential for COVID-19 diagnosis and prognosis. Deep learning techniques can...
Artificial Evolution Network: A Computational Perspective on the Expansibility of the Nervous System.
IEEE transactions on neural networks and learning systems
May 3, 2021
Neurobiologists recently found the brain can use sudden emerged channels to process information. Based on this finding, we put forward a question whether we can build a computation model that is able to integrate a sudden emerged new type of perceptu...
Qualitative Analysis and Bifurcation in a Neuron System With Memristor Characteristics and Time Delay.
IEEE transactions on neural networks and learning systems
May 3, 2021
This article focuses on the hybrid effects of memristor characteristics, time delay, and biochemical parameters on neural networks. First, we propose a novel neuron system with memristor and time delays in which the memristor is characterized by a sm...
Using Eye Gaze to Enhance Generalization of Imitation Networks to Unseen Environments.
IEEE transactions on neural networks and learning systems
May 3, 2021
Vision-based autonomous driving through imitation learning mimics the behavior of human drivers by mapping driver view images to driving actions. This article shows that performance can be enhanced via the use of eye gaze. Previous research has shown...
Local-Global Memory Neural Network for Medication Prediction.
IEEE transactions on neural networks and learning systems
Apr 2, 2021
Electronic medical records (EMRs) play an important role in medical data mining and sequential data learning. In this article, we propose to use a sequential neural network with dynamic content-based memories to predict future medications, given EMRs...
Efficient Computation Reduction in Bayesian Neural Networks Through Feature Decomposition and Memorization.
IEEE transactions on neural networks and learning systems
Apr 2, 2021
The Bayesian method is capable of capturing real-world uncertainties/incompleteness and properly addressing the overfitting issue faced by deep neural networks. In recent years, Bayesian neural networks (BNNs) have drawn tremendous attention to artif...
A Survey on Learning-Based Approaches for Modeling and Classification of Human-Machine Dialog Systems.
IEEE transactions on neural networks and learning systems
Apr 2, 2021
With the rapid development from traditional machine learning (ML) to deep learning (DL) and reinforcement learning (RL), dialog system equipped with learning mechanism has become the most effective solution to address human-machine interaction proble...
Bipartite Synchronization of Multiple Memristor-Based Neural Networks With Antagonistic Interactions.
IEEE transactions on neural networks and learning systems
Apr 2, 2021
In this article, by introducing a signed graph to describe the coopetition interactions among network nodes, the mathematical model of multiple memristor-based neural networks (MMNNs) with antagonistic interactions is established. Since the cooperati...
Finite-Time Tracking Control for Nonlinear Systems via Adaptive Neural Output Feedback and Command Filtered Backstepping.
IEEE transactions on neural networks and learning systems
Apr 2, 2021
This article is concerned with the tracking control problem for uncertain high-order nonlinear systems in the presence of input saturation. A finite-time control strategy combined with neural state observer and command filtered backstepping is propos...
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