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Found 4 papers, 0 papers with code
Real-Time Progressive Learning: Accumulate Knowledge from Control with Neural-Network-Based Selective Memory
Memory, as the basis of learning, determines the storage, update and forgetting of knowledge and further determines the efficiency of learning.
Selective Memory Recursive Least Squares: Recast Forgetting into Memory in RBF Neural Network Based Real-Time Learning
In radial basis function neural network (RBFNN) based real-time learning tasks, forgetting mechanisms are widely used such that the neural network can keep its sensitivity to new data.
A machine-learning-based tool for last closed-flux surface reconstruction on tokamaks
Tokamaks allow to confine fusion plasma with magnetic fields and one of the main challenges in the control of the magnetic configuration is the prediction/reconstruction of the Last Closed-Flux Surface (LCFS).
Experiment data-driven modeling of tokamak discharge in EAST
By using the data-driven methodology, we exploit the temporal sequence of control signals for a large set of EAST discharges to develop a deep learning model for modeling discharge diagnostic signals, such as electron density $n_{e}$, store energy $W_{mhd}$ and loop voltage $V_{loop}$.
