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Learning chaotic attractors by neural networks
Rembrandt Bakker
, Jaap C. Schouten
, C. Lee Giles
, Floris Takens
, Cor M. Van den Bleek
College of Information Sciences and Technology
Computer Science and Engineering
Supply Chain and Information Systems
Research output
:
Contribution to journal
›
Article
›
peer-review
84
Scopus citations
Overview
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Keyphrases
Neural Network
100%
Prediction Error
100%
Chaotic Attractor
100%
Data Reduction
50%
Dimensionality Reduction
50%
Chaotic Dynamics
50%
Three-state
50%
Error Propagation
50%
Short-term Prediction
50%
Prediction Horizon
50%
Geographically Weighted Principal Component Analysis (GWPCA)
50%
Short Term Predict
50%
Chaotic Characteristics
50%
Chaotic Pendulum
50%
Laser Data
50%
Reconstructed Attractor
50%
Santa Fe
50%
Computer Science
Neural Network
100%
State Variable
100%
Prediction Error
100%
Component Analysis
50%
Principal Components
50%
Data Reduction
50%
Term Prediction
50%
Mathematics
State Variable
100%
Neural Network
100%
Prediction Error
100%
Time Step
50%
Data Reduction
50%
Principal Component Analysis
50%
Chemical Engineering
Neural Network
100%
Engineering
Laser Data
50%