AIMC Journal:
IEEE transactions on neural networks and learning systems

Showing 531 to 540 of 811 articles

Adaptive Tracking Control of State Constraint Systems Based on Differential Neural Networks: A Barrier Lyapunov Function Approach.

IEEE transactions on neural networks and learning systems Nov 30, 2020
The aim of this article is to investigate the trajectory tracking problem of systems with uncertain models and state restrictions using differential neural networks (DNNs). The adaptive control design considers the design of a nonparametric identifie...
Nonlinear Dynamics Finite Element Analysis Uncertainty Models, Theoretical Robotics Neural Networks, Computer Algorithms Computer Simulation
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Circular Complex-Valued GMDH-Type Neural Network for Real-Valued Classification Problems.

IEEE transactions on neural networks and learning systems Nov 30, 2020
Recently, applications of complex-valued neural networks (CVNNs) to real-valued classification problems have attracted significant attention. However, most existing CVNNs are black-box models with poor explanation performance. This study extends the ...
Humans Neural Networks, Computer Algorithms Databases, Factual Machine Learning Data Interpretation, Statistical Benchmarking Animals Classification Models, Theoretical
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Synchronization of Coupled Time-Delay Neural Networks With Mode-Dependent Average Dwell Time Switching.

IEEE transactions on neural networks and learning systems Nov 30, 2020
In the literature, the effects of switching with average dwell time (ADT), Markovian switching, and intermittent coupling on stability and synchronization of dynamic systems have been extensively investigated. However, all of them are considered sepa...
Algorithms Probability Neural Networks, Computer Stochastic Processes Markov Chains
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Stochastic Finite-Time H State Estimation for Discrete-Time Semi-Markovian Jump Neural Networks With Time-Varying Delays.

IEEE transactions on neural networks and learning systems Nov 30, 2020
In this article, the finite-time H state estimation problem is addressed for a class of discrete-time neural networks with semi-Markovian jump parameters and time-varying delays. The focus is mainly on the design of a state estimator such that the co...
Stochastic Processes Finite Element Analysis Algorithms Neural Networks, Computer Markov Chains
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Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons.

IEEE transactions on neural networks and learning systems Nov 30, 2020
This article proposes an unsupervised address event representation (AER) object recognition approach. The proposed approach consists of a novel multiscale spatio-temporal feature (MuST) representation of input AER events and a spiking neural network ...
Pattern Recognition, Automated Electronic Data Processing Models, Neurological Neurons Neural Networks, Computer Artificial Intelligence Databases, Factual Computer Simulation Algorithms Humans
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Why ResNet Works? Residuals Generalize.

IEEE transactions on neural networks and learning systems Nov 30, 2020
Residual connections significantly boost the performance of deep neural networks. However, few theoretical results address the influence of residuals on the hypothesis complexity and the generalization ability of deep neural networks. This article st...
Neural Networks, Computer Deep Learning Generalization, Psychological Algorithms
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An Accelerated Finite-Time Convergent Neural Network for Visual Servoing of a Flexible Surgical Endoscope With Physical and RCM Constraints.

IEEE transactions on neural networks and learning systems Nov 30, 2020
This article designs and analyzes a recurrent neural network (RNN) for the visual servoing of a flexible surgical endoscope. The flexible surgical endoscope is based on a commercially available UR5 robot with a flexible endoscope attached as an end-e...
Neural Networks, Computer Algorithms Reproducibility of Results Vision, Ocular Robotics Endoscopy Mechanical Phenomena Equipment Design Biomechanical Phenomena Robotic Surgical Procedures Endoscopes
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PID Controller-Based Stochastic Optimization Acceleration for Deep Neural Networks.

IEEE transactions on neural networks and learning systems Nov 30, 2020
Deep neural networks (DNNs) are widely used and demonstrated their power in many applications, such as computer vision and pattern recognition. However, the training of these networks can be time consuming. Such a problem could be alleviated by using...
Natural Language Processing Stochastic Processes Visual Prosthesis Databases, Factual Deep Learning Neural Networks, Computer Algorithms
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DACH: Domain Adaptation Without Domain Information.

IEEE transactions on neural networks and learning systems Nov 30, 2020
Domain adaptation is becoming increasingly important for learning systems in recent years, especially with the growing diversification of data domains in real-world applications, such as the genetic data from various sequencing platforms and video fe...
Deep Learning Machine Learning Neural Networks, Computer Data Interpretation, Statistical Algorithms Image Interpretation, Computer-Assisted
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Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks.

IEEE transactions on neural networks and learning systems Nov 13, 2019
For a brain-computer interface (BCI) system, a calibration procedure is required for each individual user before he/she can use the BCI. This procedure requires approximately 20-30 min to collect enough data to build a reliable decoder. It is, theref...
Electroencephalography Brain-Computer Interfaces Movement Deep Learning Bayes Theorem Imagination Female Information Theory Reproducibility of Results Algorithms Humans Adult Databases, Factual Young Adult Male Neural Networks, Computer Brain
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