Abstract:
It is known that the implementation technology of recognition problems, based on the classic neural network, has a number of difficulties such as the need to have a large training set; the duration and complexity of learning algorithms; difficulty with the choice of such network design parameters as the number of neurons, layers, links, as well as ways to connect neurons; there may be no successful learning, with the need to re-change the network settings and re-training. In this paper we consider the possibility of creating a multi-layer perceptron with a full system of connections and with a threshold activation function on the basis of algorithms metric methods of recognition and in particular the nearest neighbor algorithm. It is shown that this method allows you to create a fully connected multilayer perceptron, such parameters of which as the number of neurons, layers, as well as the value of the weights and thresholds, are determined analytically. The distribution of weight and threshold values for the second and third layer is also discussed. On this basis, we have proposed an algorithm for calculating the thresholds and weights of a multilayer perceptron and showed an example of its implementation. The possible applications of the network for different tasks are considered.
Keywords:neural network architecture; nearest neighbor; multilayer perceptron; neural network training; linear neural network; training set.
Fulltext:
PDF file (1545 kB)