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Found 4 papers, 0 papers with code
Tuning the activation function to optimize the forecast horizon of a reservoir computer
By using different functions from this set, and by modifying their parameters, we explore whether the entropy of node activation levels or the curvature of the activation functions determine the predictive ability of the reservoirs.
Detecting disturbances in network-coupled dynamical systems with machine learning
Identifying disturbances in network-coupled dynamical systems without knowledge of the disturbances or underlying dynamics is a problem with a wide range of applications.
Suppressing unknown disturbances to dynamical systems using machine learning
In this Letter, we present a model-free method to identify and suppress an unknown disturbance to an unknown system based only on previous observations of the system under the influence of a known forcing function.
Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links
Our technique leverages the results of a machine learning process for short time prediction to achieve our goal.
