Performance predictions of ground source heat pump system based on random forest and back propagation neural network models

2019 ◽  
Vol 197 ◽  
pp. 111864 ◽  
Author(s):  
Shilei Lu ◽  
Qiaoping Li ◽  
Li Bai ◽  
Ran Wang
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Ground Source Heat Pump ◽  
Neural Network Models ◽  
Pump System ◽  
Heat Pump System ◽  
Ground Source ◽  
Performance Predictions

scholarly journals Stock Market Prediction Using Artificial Neural Networks

2012 ◽  
Vol 6-7 ◽  
pp. 1055-1060 ◽  
Author(s):  
Yang Bing ◽  
Jian Kun Hao ◽  
Si Chang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Stock Exchange ◽  
Back Propagation ◽  
Composite Index ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Neural Network Models ◽  
The Neural Network

In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.

scholarly journals Experimental and modelling studies on antifungal compounds

2006 ◽  
Vol 4 (3) ◽  
pp. 428-439 ◽  
Author(s):  
Mukesh Doble ◽  
K. Kumar
Keyword(s):  
Neural Network ◽  
Aspergillus Niger ◽  
Organic Compounds ◽  
Back Propagation ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Neural Network Models ◽  
The Neural Network ◽  
Qsar Models ◽  
Modelling Studies

AbstractAntifungal activity of organic compounds (aromatic, salicylic derivatives, cinnamyl derivatives etc) on Fusarium Rosium (14 compounds) and Aspergillus niger (17 compounds) was studied and QSAR models were developed relating molecular descriptors with the observed activity. Back propagation Neural Network models and single and multiple regression models were tested for predicting the observed activity. The data fit as well as the predictive capability of the neural network models were satisfactory (R2 = 0.84, q2 = 0.73 for Fusarium Rosium and R2 = 0.75, q2 = 0.62 for Aspergillus niger). The descriptors used in the network for the former were X4 (connectivity) and Jhetv (topological); and TIC1 (information) and SPI (topological) for the latter fungus. Antifungal activities of these organic compounds were generally lower against the latter than with the former fungus.

Sign in / Sign up

Export Citation Format

Share Document