VLSI Realization of Artificial Neural Networks with Improved Fault Tolerance

2010 ◽  
Vol 2 (9) ◽  
pp. 32-37
Author(s):  
M Nirmala Devi ◽  
N Mohankumar ◽  
Jayalakshmi P Nair
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Tolerance ◽  
Artificial Neural

Numerical Simulation of Neural Network Components of Controlling and Measuring Systems

2015 ◽  
Vol 756 ◽  
pp. 507-512
Author(s):  
S.N. Danilin ◽  
M.V. Makarov ◽  
S.A. Shchanikov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Tolerance ◽  
Feedforward Neural Networks ◽  
Measuring Systems ◽  
Tolerance Level ◽  
Artificial Neural ◽  
And Function

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.

A Matlab Tool for Analyzing and Improving Fault Tolerance of Artificial Neural Networks

2009 ◽  
Vol 42 (19) ◽  
pp. 158-163 ◽  
Author(s):  
Rui Borralho ◽  
Pedro Fontes ◽  
Ana Antunes ◽  
Fernando Morgado Dias
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Tolerance ◽  
Artificial Neural

DEVELOPMENT OF METHODS FOR DETERMINING AND ENSURING RELIABILITY OF ARTIFICIAL NEURAL NETWORKS BASED ON MEMRISTIVE DEVICES

2021 ◽  
pp. 20-26
Author(s):  
S. N. Danilin ◽  
◽  
S. A. Shchanikov ◽  
I. A. Bordanov ◽  
A.D. Zuev ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Tolerance ◽  
Hardware Implementation ◽  
System Engineering ◽  
Artificial Neural

The article is devoted to the reliability of hardware implementation of artificial neural networks based on memristive devices (ANNM). On the basis of the system engineering methodology, the authors have developed a universal general approach to determining the reliability of ANNM “from the accuracy of functioning through fault tolerance”. The active and passive methods of ensuring the reliability of ANNM are described. An example of determining and ensuring the reliability of a specific version of the hardware implementation of ANNM is given.

SEU fault tolerance in artificial neural networks

1995 ◽  
Vol 42 (6) ◽  
pp. 1856-1862 ◽  
Author(s):  
R. Velazco ◽  
A. Assoum ◽  
N.E. Radi ◽  
R. Ecoffet ◽  
X. Botey
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Tolerance ◽  
Artificial Neural
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