Artificial neural network, training set, data Neural networks, Hebbian learning, irrelevant items, patterns optimization, pattern preprocessing

The article deals with possibilities of optimization of classifiers based on neural networks which use Hebbian learning mechanism. The experimental study was conducted. The study shows, that badly designed learning patterns can prevent the network from learning under certain circumstances. The new term of irrelevant items of input vectors has been introduced in the article. Also we have introduced a optimization method. This method helps to avoid problems caused by so-called irrelevant items of input vectors and thus makes the learning algorithm more robust. The method lays off the self classifying algorithm. Thanks to the fact it is very easy to equip any arbitrary algorithm with it.