Simulation of hysteresis loops for a superconductor using neural networks with Kalman filtering

Agnieszka Krok

Agnieszka Krok Cracow University of Technology

Abstract

Kalman filtering is used as a learning method for the training of Feed-forward Layered Neural Networks (FLNN) and Recurrent LNNs (RLNN). These networks were applied to the simulation of hysteresis loops obtained by the experiment on a cable-in-conduit superconductors by the test carried out in a cryogenic press [8] . The training and testing patterns were taken from nine selected, characteristic hysteresis loops. The formulated FLNN: 4-4-5-1 gives the computer simulation of higher accuracy than the standard network FLNN: 4-7-5-1 discussed in [5].

Keywords

Kalman filtering, neural network, simulation, conductor, hysteresis loops,

References

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