Please use this identifier to cite or link to this item:
Title: Методы прогноза содержания взрывоопасных газов в горных выработках на основе динамических нейронных сетей
Other Titles: Methods of forecasting of explosive gases content in mine working based on dynamic neural networks
Authors: Дикова, Юлия Леонидовна
Федоров, Евгений Евгеньевич
Цололо, Сергей Алексеевич
Keywords: forecast
dynamic neural networks
parameter adaptation
clonal selection algorithm
simulated annealing
Issue Date: 17-May-2015
Abstract: Some methods of forcasting of explosive gases content in mine workings based on dynamic simulated neutral network were developed and implemented in order to improve safety operations in mines. The methods include a nonlinear autoregressive with exogenous inputs (NARX) and a Time-Lagged Feedforward Neural Network (TLFN). The selection of network architecture (determination of the number of neurons in the hidden layer and the lenght of the delay) was done basing on the minimum value of the mean square error.The adaptation of the parameters of the suggested networks was based on metaheuristic algorithms for clonal selection of two types: with the use of simulated annealing and without it. In order to evaluate the effectiveness of the suggested methods numerical studies, which prove the effectiveness of the selected networks applicable to the specific conditions of use, were done.
ISSN: 2310-9017
Appears in Collections:Наукові праці співробітників кафедри комп'ютерних наук

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.