Numerical simulation of neural network components of controlling and measuring systems

Author

Danilin, S.N. ; Makarov, M.V. ; Shchanikov, S.A.

Author_Institution

Dept. of CAD Syst., Murom Inst. of Vladimir State Univ., Murom, Russia

fYear

2014

fDate

16-18 Oct. 2014

Firstpage

1

Lastpage

4

Abstract

The article deals with the problem of calculating the fault tolerance of neural network components of industrial controlling and measuring systems used in mechanical engineering. We have formulated a general approach to developing methods for quantitative determination of the level of the fault tolerance of artificial neural networks with any structure and function. We have studied the fault tolerance of four artificial feedforward neural networks as well as the correlation between the result of determining the fault tolerance level and a selected performance parameter of artificial neural networks.

Keywords

condition monitoring; fault tolerance; feedforward neural nets; industrial control; measurement systems; mechanical engineering computing; neural nets; artificial feedforward neural network; controlling and measuring system; fault tolerance; mechanical engineering; neural network component; Artificial neural networks; Mathematics; Mechanical variables measurement; Training; control and measuring systems; fault tolerance; neural networks; the quality of the neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechanical Engineering, Automation and Control Systems (MEACS), 2014 International Conference on

Conference_Location

Tomsk

Print_ISBN

978-1-4799-6220-4

Type

conf

DOI

10.1109/MEACS.2014.6986873

Filename

6986873