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
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;
Conference_Titel :
Soft Computing and Measurements (SCM), 2015 XVIII International Conference on
Conference_Location :
St. Petersburg
Print_ISBN :
978-1-4673-6960-2
DOI :
10.1109/SCM.2015.7190434
