Considering adequacy in neural network learning

We propose a new learning strategy to consider aspects of cognitive adequacy during the training of artificial neural networks instead of merely taking the overall error into account. Well known learning algorithms for neural networks can be adapted in a way that leads to an adequate behaviour by using a fuzzy system to provide pattern specific learning rates based on a predetermined measure of pattern difficulty and the current classification error. First experiments with adequate backpropagation show that adequate learning provides faster generalization-error convergence than its conventional counterpart