scholarly journals Short-term Prediction of the CSI 300 Based on the BP Neural Network Model

2020 ◽  
Vol 1437 ◽  
pp. 012054
Author(s):  
Shunyu Ning
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction ◽  
Bp Neural Network Model

Prediction Models of Short-Term Traffic Flow Based on Neural Network

2013 ◽  
Vol 671-674 ◽  
pp. 2908-2911 ◽  
Author(s):  
Chao Jun Dong ◽  
Ang Cui
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Topology ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Measured Data ◽  
Short Term ◽  
Hidden Layer ◽  
Bp Neural Network Model

For the city’s road conditions, a nonlinear regression prediction model based on BP Neural Network was built. The simulation shows it has good adaptability and strong nonlinear mapping ability. Using the wavelet basis function as hidden layer nodes transfer function, a BP-Neural- Network-topology-based Wavelet Neural Network model was proposed. The model can overcome the defects of the BP Neural Network model that easy to fall into local minimum and cannot perform global search. The feasibility of the model was proved using measured data from yingbin avenue in jiangmen city.

scholarly journals A neural network model for short term prediction of surface ozone at Pone

MAUSAM ◽  
2021 ◽  
Vol 50 (1) ◽  
pp. 91-98
Author(s):  
P. GUHATHAKURTA
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Network Model ◽  
Neural Network Model ◽  
Surface Ozone ◽  
Anthropogenic Activity ◽  
Short Term ◽  
Term Prediction ◽  
The Neural Network ◽  
Short Term Prediction

A new method for short term prediction of air pollution is presented using the neural network technique, Due to increase in industrial and anthropogenic activity, air pollution is a serious subject of concern today, Surface ozone can be considered as a representative of total atmospheric oxidants and of air pollution, A three layer neural network model using the technique of adaptive pattern recognition is developed, The model can predict the mean surface ozone between 12 and 13 hours (hour of maximum concentration), The model can perform well both in training and independent periods, The classical methods of short term modelling are not reliable enough, The method can also be used for short term prediction of other air pollutants.

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