A Nonlinear Artificial Intelligence Ensemble Prediction Model Based on EOF for Typhoon Track

2011 ◽  
Author(s):  
Xiao-yan Huang ◽  
Long Jin ◽  
Xv-ming Shi
Keyword(s):  
Artificial Intelligence ◽  
Prediction Model ◽  
Ensemble Prediction ◽  
Model Based ◽  
Typhoon Track

scholarly journals Prediction of supercritical carbon dioxide solubility in polymers based on hybrid artificial intelligence method integrated with the diffusion theory

RSC Advances ◽  
2017 ◽  
Vol 7 (78) ◽  
pp. 49817-49827 ◽  
Author(s):  
Li Mengshan ◽  
Liu Liang ◽  
Huang Xingyuan ◽  
Liu Hesheng ◽  
Chen Bingsheng ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Prediction Model ◽  
Diffusion Theory ◽  
Solubility Prediction ◽  
Model Based ◽  
Hybrid Artificial Intelligence ◽  
Supercritical Carbon

A solubility prediction model based on a hybrid artificial intelligence method integrated with diffusion theory is proposed.

scholarly journals A Nonlinear Artificial Intelligence Ensemble Prediction Model for Typhoon Intensity

2008 ◽  
Vol 136 (12) ◽  
pp. 4541-4554 ◽  
Author(s):  
Long Jin ◽  
Cai Yao ◽  
Xiao-Yan Huang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Prediction Model ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Neural Network Approach ◽  
Multiple Neural Networks ◽  
Typhoon Intensity ◽  
Genetic Neural Network ◽  
Better Than

Abstract A new nonlinear artificial intelligence ensemble prediction (NAIEP) model has been developed for predicting typhoon intensity based on multiple neural networks with the same expected output and using an evolutionary genetic algorithm (GA). The model is validated with short-range forecasts of typhoon intensity in the South China Sea (SCS); results show that the NAIEP model is clearly better than the climatology and persistence (CLIPER) model for 24-h forecasts of typhoon intensity. Using identical predictors and sample cases, predictions of the genetic neural network (GNN) ensemble prediction (GNNEP) model are compared with the single-GNN prediction model, and it has been proven theoretically that the former is more accurate. Computation and analysis of the generalization capacity of GNNEP also demonstrate that the prediction of the ensemble model integrates predictions of its optimized ensemble members, so the generalization capacity of the ensemble prediction model is also enhanced. This model better addresses the “overfitting” problem that generally exists in the traditional neural network approach to practical weather prediction.

Online Prediction Model Based on New Kernel Method

2014 ◽  
Vol 7 (1) ◽  
pp. 107
Author(s):  
Ilyes Elaissi ◽  
Okba Taouali ◽  
Messaoud Hassani
Keyword(s):  
Prediction Model ◽  
Kernel Method ◽  
Model Based ◽  
Online Prediction
Sign in / Sign up

Export Citation Format

Share Document