scholarly journals Efficient Hardware Implementation of Nonlinear Moving-horizon State Estimation with Artificial Neural Networks

2020 ◽  
Vol 53 (2) ◽  
pp. 7813-7818
Author(s):  
Rafael Koji Vatanabe Brunello ◽  
Renato Coral Sampaio ◽  
Carlos H Llanos ◽  
Leandro dos Santos Coelho ◽  
Helon Vicente Hultmann Ayala
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
State Estimation ◽  
Hardware Implementation ◽  
Artificial Neural

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.

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