Open AccessThe influence of algorithms for tuning the parameters of neuromorphic systems on their fault tolerance
Author(s) -
S.N. Danilin,
Sergey Shchanikov,
Ilya Bordanov,
A. D. Zuev
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1333/3/032077
Subject(s) - neuromorphic engineering , memristor , artificial neural network , computer science , range (aeronautics) , fault tolerance , fault (geology) , perceptron , multilayer perceptron , algorithm , mode (computer interface) , artificial intelligence , electronic engineering , engineering , distributed computing , seismology , geology , aerospace engineering , operating system
This article describes the influence of algorithms for tuning the parameters of neuromorphic systems on their fault tolerance. This is relevant to the hardware implementation of neuromorphic systems using memristors (NSM). The study is conducted using the authors developed a variant of the system approach and methods of simulation of artificial neural networks (ANN). By the example of a multilayer perceptron, it is shown that different ANN learning algorithms in the nominal mode of operation make it possible to achieve similar values of the operation accuracy. But due to the influence of production and operational factors in real conditions of operation, the ANN may fail. The range of allowable values of the destabilizing factors on ANN operation depends on the learning algorithm and may differ several times.