DocumentCode
3535019
Title
Neural network approach to forecast the state of the Internet of Things elements
Author
Kotenko, Igor ; Saenko, Igor ; Skorik, Fadey ; Bushuev, Sergey
Author_Institution
St. Petersburg Inst. of Inf. & Autom., St. Petersburg, Russia
fYear
2015
fDate
19-21 May 2015
Firstpage
133
Lastpage
135
Abstract
The paper presents the method to forecast the states of elements of the Internet of Things based on using an artificial neural network. The offered architecture of the neural network is a combination of a multilayered perceptron and a probabilistic neural network. For this reason, it provides high efficiency of decision-making. Results of an experimental assessment of the offered neural network on the accuracy of forecasting the states of elements of the Internet of Things are discussed.
Keywords
Internet of Things; decision making; multilayer perceptrons; neural net architecture; probability; Internet of Things; artificial neural network; decision making; multilayered perceptron; probabilistic neural network; Artificial neural networks; Computer architecture; Forecasting; Internet of things; Probabilistic logic; Security; internet of things; multilayered perceptron; neural network; state monitoring;
fLanguage
English
Publisher
ieee
Conference_Titel
Soft Computing and Measurements (SCM), 2015 XVIII International Conference on
Conference_Location
St. Petersburg
Print_ISBN
978-1-4673-6960-2
Type
conf
DOI
10.1109/SCM.2015.7190434
Filename
7190434
Link To Document