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.

The Faulty Behavior of Feedforward Neural Networks with Hard-Limiting Activation Function

1997 ◽  
Vol 9 (5) ◽  
pp. 1109-1126
Author(s):  
Zhiyu Tian ◽  
Ting-Ting Y. Lin ◽  
Shiyuan Yang ◽  
Shibai Tong
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Monte Carlo Simulations ◽  
Hardware Implementation ◽  
Fault Tolerant ◽  
Feedforward Neural Networks ◽  
Activation Function ◽  
Artificial Neural

With the progress in hardware implementation of artificial neural networks, the ability to analyze their faulty behavior has become increasingly important to their diagnosis, repair, reconfiguration, and reliable application. The behavior of feedforward neural networks with hard limiting activation function under stuck-at faults is studied in this article. It is shown that the stuck-at-M faults have a larger effect on the network's performance than the mixed stuck-at faults, which in turn have a larger effect than that of stuck-at-0 faults. Furthermore, the fault-tolerant ability of the network decreases with the increase of its size for the same percentage of faulty interconnections. The results of our analysis are validated by Monte-Carlo simulations.

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.

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