Neural Network Approximation of Characteristics of Multi-Channel Non-Markovian Queuing Systems

Abstract

It is proposed to use a neural network to calculate an approximation of the probabilistic-time characteristics of multichannel queuing systems (QS) with a "warm-up" and the unlimited capacity of the queue. From the results of numerical experiments, we observe a significant reduction in the complexity of computing probabilistic-time characteristics of the multi-channel QS with "warm-up" with minor errors of calculation of characteristics, compared with the numerical iterative algorithms. The advisability of the use of Bayesian regularization method for training a neural network and the best number of neurons are shown.

Indoor Mobile Positioning Using Neural Networks and Fuzzy Logic Control

“…By analogy with [16], we make a comparative analysis of the main learning algorithms of the neural network used for the navigation of mobile applications for indoors, with position accuracy and complexity ratios depending on the number of neurons in the hidden layer. The corresponding results are shown in Table 4.…”