Features of Neural Network Classifier Models for Object Recognition

Abstract

In decision support subsystems for object recognition, the detection of the most probable result among those possible for a given set of features is of particular importance. For this purpose, it is appropriate to assign specific ranks to each of the resulting signals in the classification process. In this article, two models of the neural network classifier are considered, and the result of classification in the improved model is the formation of ranks for all defined classes using a new approach. So, the functionality of such a neural network classifier, in this case, was expanded due to the ranking of classes. The advanced neural network classifier has five layers — input, three hidden ones, and output layers. In the first hidden layer, the corresponding discriminant functions are formed, in the second hidden layer, the WTA competition mechanism is implemented (the winner takes all). The output layer, in which the object class ranks are formed, is built on counters in which the class ranks are gradually calculated. The third hidden layer acts as a masking layer, participating in the formation of ranks. Therefore, the introduction of two layers - masking and output in the form of counters — allows to determine the ranks of the input object in relation to its belonging to specific classes. The article presents the general structures of the considered neural network classifiers, shows the topological structures of both models of such classifiers for comparison, and also considers the functional scheme of the elements of the added layers. Features of the functioning of the proposed classifier are presented, and its structural and functional characteristics are presented in the form of a table. In addition, the peculiarities of the implementation process of the neuron competition mechanism in the competitive layer of the classifier are schematically shown.