Results 21 to 30 of about 1,476,903 (264)
CAAI Transactions on Intelligence Technology, 2023 Manipulators actuate joints to let end effectors to perform precise path tracking tasks. Recurrent neural network which is described by dynamic models with parallel processing capability, is a powerful tool for kinematic control of manipulators.
Zhan Li, Shuai Li
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Frontiers in Human Neuroscience, 2021 It was meaningful to predict the customers' decision-making behavior in the field of market. However, due to individual differences and complex, non-linear natures of the electroencephalogram (EEG) signals, it was hard to classify the EEG signals and to ...
Qingguo Ma +7 more
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An attractor-based complexity measurement for Boolean recurrent neural networks. [PDF]
PLoS ONE, 2014 We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics.
Jérémie Cabessa, Alessandro E P Villa
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A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
Neural Computation, 2019 Recurrent neural networks (RNNs) have been widely adopted in research areas concerned with sequential data, such as text, audio, and video. However, RNNs consisting of sigma cells or tanh cells are unable to learn the relevant information of input data ...
Yong Yu +3 more
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Effect of Bodybuilding and Fitness Exercise on Physical Fitness Based on Deep Learning
Emergency Medicine International, 2022 With the rapid development of society and economy, people’s living standards are improving day by day, and increasingly attention is paid to physical health, which has set off a fitness upsurge.
Manman Sun, Lijun Wang
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3-D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2020 In this article, we propose an alternating directional 3-D quasi-recurrent neural network for hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge—structural spatiospectral correlation and global correlation along ...
Kaixuan Wei, Ying Fu, Hua Huang
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HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System
Processes, 2021 Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks ...
Muhammad Ashfaq Khan
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Deductron—A Recurrent Neural Network
Frontiers in Applied Mathematics and Statistics, 2020 The current paper is a study in Recurrent Neural Networks (RNN), motivated by the lack of examples simple enough so that they can be thoroughly understood theoretically, but complex enough to be realistic.
Marek Rychlik
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Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java
JITeCS (Journal of Information Technology and Computer Science), 2021 Rice is the staple food for most of the population in Indonesia which is processed from rice plants. To meet the needs and food security in Indonesia, a prediction is required.
Andi Hamdianah +2 more
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Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting [PDF]
IEEE transactions on intelligent transportation systems (Print), 2018 Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks.
Zhiyong Cui +3 more
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