  A neural network model, EFITNN, has been developed capable of real-time
magnetic equilibrium reconstruction based on HL-3 tokamak magnetic measurement
signals. The model processes inputs from 68 channels of magnetic measurement
data gathered from 1159 HL-3 experimental discharges, including plasma current,
loop voltage, and the poloidal magnetic fields measured by equilibrium probes.
The outputs of the model feature eight key plasma parameters, alongside
high-resolution ($129\times129$) reconstructions of the toroidal current
density $J_{\text P}$ and poloidal magnetic flux profiles $\Psi_{rz}$.
Moreover, the network's architecture employs a multi-task learning structure,
which enables the sharing of weights and mutual correction among different
outputs, and lead to increase the model's accuracy by up to 32%. The
performance of EFITNN demonstrates remarkable consistency with the offline
EFIT, achieving average $R^2 = 0.941, 0.997$ and $0.959$ for eight plasma
parameters, $\Psi_{rz}$ and $J_{\text P}$, respectively. The model's robust
generalization capabilities are particularly evident in its successful
predictions of quasi-snowflake (QSF) divertor configurations and its adept
handling of data from shot numbers or plasma current intervals not previously
encountered during training. Compared to numerical methods, EFITNN
significantly enhances computational efficiency with average computation time
ranging from 0.08ms to 0.45ms, indicating its potential utility in real-time
isoflux control and plasma profile management.
