Optical Signal-to-Noise Ratio Prediction using Neural Networks for Multicast Light-Trees in Optical Networks

2019 ◽  
Author(s):  
Lu Zhang ◽  
Xin Li ◽  
Tao Gao ◽  
Ying Tang ◽  
Yongjun Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Optical Networks ◽  
Signal To Noise Ratio ◽  
Optical Signal ◽  
Signal To Noise ◽  
Noise Ratio

A Novel Restoration Algorithm Based on Optical Signal-to-Noise Ratio for Transparent Optical Networks

2011 ◽  
Author(s):  
Rodrigo Freitas ◽  
Rodrigo Silva ◽  
Helder Pereira ◽  
Daniel Chaves ◽  
Carmelo Bastos-Filho ◽  
...  
Keyword(s):  
Optical Networks ◽  
Signal To Noise Ratio ◽  
Optical Signal ◽  
Signal To Noise ◽  
Transparent Optical Networks ◽  
Restoration Algorithm ◽  
Noise Ratio

Analysis of neural networks efficiency when accounting for weight coefficients jitter, leading to reduction in output signal/noise ratio

2021 ◽  
Author(s):  
S.V. Zimina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Useful Signal ◽  
Artificial Neural ◽  
Noise Ratio ◽  
Weight Coefficients

Setting up artificial neural networks using iterative algorithms is accompanied by fluctuations in weight coefficients. When an artificial neural network solves the problem of allocating a useful signal against the background of interference, fluctuations in the weight vector lead to a deterioration of the useful signal allocated by the network and, in particular, losses in the output signal-to-noise ratio. The goal of the research is to perform a statistical analysis of an artificial neural network, that includes analysis of losses in the output signal-to-noise ratio associated with fluctuations in the weight coefficients of an artificial neural network. We considered artificial neural networks that are configured using discrete gradient, fast recurrent algorithms with restrictions, and the Hebb algorithm. It is shown that fluctuations lead to losses in the output signal/noise ratio, the level of which depends on the type of algorithm under consideration and the speed of setting up an artificial neural network. Taking into account the fluctuations of the weight vector in the analysis of the output signal-to-noise ratio allows us to correlate the permissible level of loss in the output signal-to-noise ratio and the speed of network configuration corresponding to this level when working with an artificial neural network.

scholarly journals Integrated in-band optical signal-to-noise ratio monitor implemented on SOI platform

2012 ◽  
Vol 20 (8) ◽  
pp. 8512 ◽  
Author(s):  
Lianxi Jia ◽  
Junfeng Song ◽  
Tsung-Yang Liow ◽  
Qing Fang ◽  
Mingbin Yu ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Optical Signal ◽  
Signal To Noise ◽  
Noise Ratio

Optical signal-to-noise ratio monitoring using uncorrelated beat noise

2005 ◽  
Vol 17 (11) ◽  
pp. 2484-2486 ◽  
Author(s):  
Wei Chen ◽  
R.S. Tucker ◽  
Xingwen Yi ◽  
W. Shieh ◽  
J.S. Evans
Keyword(s):  
Signal To Noise Ratio ◽  
Optical Signal ◽  
Signal To Noise ◽  
Beat Noise ◽  
Noise Ratio
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