articleTop 10% cited
Approximation of dynamical systems by continuous time recurrent neural networks
Neural Networks · 1993 · Vol. 6(6) · pp. 801–806
Ken-ichi Funahashi✉(Toyohashi University of Technology)Yuichi Nakamura(Toyohashi University of Technology)
Neural Networks and ApplicationsModel Reduction and Neural NetworksControl Systems and IdentificationRecurrent neural networkCorollaryArtificial neural networkDynamical system (definition)Dynamical systems theoryTrajectoryComputer scienceLinear dynamical systemControl theory (sociology)Mathematics
Citations
969
FWCI
10.45
field-weighted impact
References
19
Percentile
98%
vs. same field & year
Citations per year
Cited by
Deep learning and its applications to machine health monitoring
Mechanical Systems and Signal Processing · 2018 · 2,497 citations
A systematic review of convolutional neural network-based structural condition assessment techniques
Engineering Structures · 2020 · 371 citations
Bidirectional LSTM with attention mechanism and convolutional layer for text classification
Neurocomputing · 2019 · 1,101 citations
A Hybrid Deep Learning Model With Attention-Based Conv-LSTM Networks for Short-Term Traffic Flow Prediction
IEEE Transactions on Intelligent Transportation Systems · 2020 · 499 citations
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features
IEEE Access · 2017 · 741 citations
References
On the approximate realization of continuous mappings by neural networks
Neural Networks · 1989 · 4,195 citations
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
Neural Computation · 1989 · 4,409 citations
Convergent activation dynamics in continuous time networks
Neural Networks · 1989 · 598 citations
Multilayer feedforward networks are universal approximators
Neural Networks · 1989 · 20,841 citations
Learning State Space Trajectories in Recurrent Neural Networks
Neural Computation · 1989 · 674 citations
Multilayer feedforward networks are universal approximators
Neural Networks · 1989 · 9,346 citations
Citation Network
How this paper connects to the literature. Drag to explore, click any node to open that paper.
