A feed forward neural network (FFNN) has been trained to discriminate between power transformer magnetizing inrush and fault currents. The training algorithm used was back-propagation, assuming initially a sigmoid transfer function for the network's processing units ("neurons"). Once the network was trained the units' transfer function was changed to hard limiters with thresholds equal to the biases obtained for the sigmoids during training. The off-line experimental results presented in this paper show that a FFNN may be considered as an alternative method to make the discrimination between inrush and fault currents in a digital relay implementation.
A description is given of CMOS electronic circuits which emulate natural neurons at a more detailed level than that typically used by artificial neural network models. A pulse-firing circuit which realizes general short-term neuron dynamics is discussed. Both fixed and programmable synapse circuits for realizing long-term dynamics are also described. Together, these establish the basic structures required for the implementation of programmable impulse neural networks.
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