American Journal of Mathematical and Computer Modelling
Volume 5, Issue 3, September 2020, Pages: 83-88
Received: Aug. 7, 2020;
Accepted: Aug. 21, 2020;
Published: Sep. 16, 2020
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Sargsyan Siranush, Department of Programming and Information Technologies, Erevan State University, Yerevan, Armenia; Department of System Programming, Russian-Armenian University, Yerevan, Armenia
Hovakimyan Anna, Department of Programming and Information Technologies, Erevan State University, Yerevan, Armenia; Department of System Programming, Russian-Armenian University, Yerevan, Armenia
Artificial neural networks are widely used to solve various applied problems. For the successful application of artificial neural networks, it is necessary to choose the correct network architecture, to select its parameters, threshold values of the elements, activation functions, etc. The problem of evaluating the neural network parameters, based on a study of the probabilistic behavior of the network is much promising. The study in the direction of developing probabilistic methods for perceptron-type pattern recognition systems is considered in different works. The concept of the characteristic function of the perceptron introduced by S. V Dayan was used by him to prove theorems on the existence of a perceptron solution. At the same time, issues of choosing a network architecture, theoretical assessment, and optimization of neural network parameters remain relevant. In this paper, we propose a mathematical apparatus for studying the relationship between the probability of correct classification of input data and the number of elements of hidden layers of a neural network. To evaluate the network performance and to estimate some parameters of the neural network such as the number of associative elements depending on the number of classification classes the mathematical expectation and variance of weights at the input of the output layer are considered. A theorem on the necessary and sufficient condition for the existence of a solution for a neural network is proved. By a solution of neural networks, the ability to recognize images with a probability other than zero is meant. The results of the proved theorem and its corollaries coincide with the results obtained by F. Rosenblat and S. Dayan for the perceptron in a different way.
One Approach to the Problem of the Existence of a Solution in Neural Networks, American Journal of Mathematical and Computer Modelling.
Vol. 5, No. 3,
2020, pp. 83-88.
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