A Comparative Study of Neural Networks and Nonlinear Time Series Techniques for Dynamic Modeling of Chemical Processes

1996 ◽  
pp. 309-330 ◽  
Author(s):  
A. Raich ◽  
X. Wu ◽  
H.-F. Lin ◽  
Ali Cinar
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Comparative Study ◽  
Dynamic Modeling ◽  
Nonlinear Time Series ◽  
Chemical Processes

scholarly journals Nonlinear Time-Series Analysis of Pulsation of Post-AGB Stars by Genetic Algorithm/Neural Network Hybrid Systems

2000 ◽  
Vol 176 ◽  
pp. 135-136
Author(s):  
Toshiki Aikawa
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Hybrid System ◽  
Nonlinear Time Series ◽  
Photometric Data ◽  
Nonlinear Time Series Analysis ◽  
Agb Stars

AbstractSome pulsating post-AGB stars have been observed with an Automatic Photometry Telescope (APT) and a considerable amount of precise photometric data has been accumulated for these stars. The datasets, however, are still sparse, and this is a problem for applying nonlinear time series: for instance, modeling of attractors by the artificial neural networks (NN) to the datasets. We propose the optimization of data interpolations with the genetic algorithm (GA) and the hybrid system combined with NN. We apply this system to the Mackey–Glass equation, and attempt an analysis of the photometric data of post-AGB variables.

Forecasting nonlinear time series with feed-forward neural networks: a case study of Canadian lynx data

2005 ◽  
Vol 24 (2) ◽  
pp. 105-117 ◽  
Author(s):  
Yoshio Kajitani ◽  
Keith W. Hipel ◽  
A. Ian Mcleod
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Nonlinear Time Series ◽  
Feed Forward ◽  
Feed Forward Neural Networks
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