Improving backpropagation with sliding mode control

Sliding mode control is applied as a procedure to adapt weights of a multilayer perceptron. Standard backpropagation weight update equations are used for providing error estimates for the output and hidden layers, similarly to the classical algorithm. The sliding mode procedures are then introduced to adapt weights taking into consideration the standard backpropagation errors. As demonstrated throughout this paper, the introduction of sliding mode has resulted in a much faster version of the standard backpropagation. The speed-up achieved is around two times the standard version.