Abstract: The present disclosure relates to a method of anomaly detection using a trained artificial neural network (502) configured to implement at least an auto-associative function for replicating an input data sample at one or more outputs (A), the method comprising: a) injecting an input data sample into the trained artificial neural network (502) in order to generate a first replicated sample at the one or more outputs (A); b) performing at least one reinjection operation; c) computing a first parameter based on a distance between a value of an nth replicated sample present at the one or more outputs and a value of one of the previously injected or reinjected values; and d) comparing the first parameter with a first threshold (?), and processing the input data sample as an anomalous data sample if the first threshold is exceeded.

Abstract: A method of training an artificial neural network, the method comprising: initially training a first artificial neural network with first input data and first pseudo data, wherein the first pseudo data is or was generated by a second artificial neural network in a virgin state, or by the first artificial neural network while in a virgin state; generating second pseudo data using the first artificial neural network, or using the second artificial neural network following at least partially transferring knowledge from the first artificial neural network to the second artificial neural network; and training the first artificial neural network, or another artificial neural network, with the second pseudo data and second input data.

Abstract: The present disclosure relates to a control system comprising: a computation device (301) implementing at least a first artificial neural network (Net_1); one or more sensors (320) configured to capture input data samples; and one or more actuators (322); wherein the computation device (301) is configured to: generate at least one pseudo-data sample by: a) iterative sampling one of the base data samples towards one of the decision boundaries to generate one or more modified base data samples; and b) selecting one or more of the modified base data samples to form the pseudo-data sample.