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Learning hidden dynamics using echo state network
Jianming Liu
, Xu Xu
,
Quan Bing Eric Li
SCEDT Engineering
Centre for Sustainable Engineering
Centre for Future Facing Learning and AI in Higher Education
Research output
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Contribution to journal
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Article
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peer-review
7
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Keyphrases
Hidden Dynamics
100%
Echo State Network
100%
Information Flow
57%
Parameter Values
42%
Prediction Accuracy
14%
Numerical Experiments
14%
Recurrent Neural Network
14%
Nonlinear Dynamics
14%
Dynamic Training
14%
Dynamic Prediction
14%
Evolutionary Behavior
14%
Three-dimensional Dynamical System
14%
Local Inputs
14%
Fourth-order Differential Equations
14%
Basin of Attraction
14%
Second Order Delay Differential Equation
14%
Fewer Parameters
14%
Mathematics
Initial Value
100%
Local Information
100%
Neural Network
25%
Numerical Experiment
25%
Differential Equation
25%
Accurate Prediction
25%
Initial State
25%
Fourth-Order
25%
Delay-Differential Equation
25%
Dimensional Dynamical System
25%
Engineering
Network State
100%
Initial Value
57%
Local Information
57%
Numerical Experiment
14%
Accurate Prediction
14%
Fourth Order
14%
Non-Linear Dynamic
14%
Singularities
14%
Initial State
14%
Input Information
14%
Recurrent Neural Network
14%
Differential Delay
14%
Order Differential Equation
14%
Computer Science
Echo State Network
100%
Local Information
57%
Initial Value
57%
Parameter Value
42%
Recurrent Neural Network
14%
Input Information
14%
Dynamical System
14%
Attraction Basin
14%