Development of a PM2.5 prediction model using a recurrent neural network algorithm for the Seoul metropolitan area, Republic of Korea

2021 ◽  
Vol 245 ◽  
pp. 118021 ◽  
Author(s):  
Ho Chang-Hoi ◽  
Ingyu Park ◽  
Hye-Ryun Oh ◽  
Hyeon-Ju Gim ◽  
Sun-Kyong Hur ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Recurrent Neural Network ◽  
Metropolitan Area ◽  
Republic Of Korea ◽  
Seoul Metropolitan Area ◽  
Network Algorithm ◽  
Neural Network Algorithm

A STUDY OF TRACK FINDING WITH A NEURAL NETWORK ALGORITHM

1992 ◽  
Vol 03 (supp01) ◽  
pp. 303-308
Author(s):  
Giuseppe Barbagli ◽  
Guido Castellini ◽  
Gregorio Landi ◽  
Stefano Vettori
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Hardware Implementation ◽  
Initial Conditions ◽  
Hopfield Model ◽  
Network Algorithm ◽  
Random Initial Conditions ◽  
Neural Network Algorithm

We have investigated the problem of track finding with a recurrent neural network algorithm based on the Hopfield model and considered the possibility of a hardware implementation with DSP’s. Starting from a set of signal points we define track segments and set a cut on the length to keep the size of the network reasonable. Those segments surviving the cut are associated to neurons. A geometric coupling of neighbouring segments is used to select smooth combinations of them. Given random initial conditions the network converges to a solution. The method may be applied to a variety of curves.

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