scholarly journals Short term prediction of COVID-19 cases by using various types of neural network model

2020 ◽  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

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.

An improved self-organizing incremental neural network model for short-term time-series load prediction

2021 ◽  
Vol 292 ◽  
pp. 116912
Author(s):  
Rong Wang Ng ◽  
Kasim Mumtaj Begam ◽  
Rajprasad Kumar Rajkumar ◽  
Yee Wan Wong ◽  
Lee Wai Chong
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Load Prediction ◽  
Short Term ◽  
Self Organizing

A Novel Quantum Neural Network Model with Variable Selection for Short Term Load Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 612-617 ◽  
Author(s):  
Wei Sun ◽  
Yu Jun He ◽  
Ming Meng
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Short Term ◽  
Bp Network ◽  
Quantum Neural Network ◽  
Short Term Load Forecasting ◽  
Selection For

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

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