A reduced structure for nonlinear modeling of time series

2005 ◽  
Author(s):  
C. Bianchini ◽  
R. Genesio ◽  
M. Nitti
Keyword(s):  
Time Series ◽  
Nonlinear Modeling

scholarly journals SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series

2017 ◽  
Vol 65 (19) ◽  
pp. 4994-5005 ◽  
Author(s):  
Qiuyi Han ◽  
Jie Ding ◽  
Edoardo M. Airoldi ◽  
Vahid Tarokh
Keyword(s):  
Time Series ◽  
Nonlinear Modeling

Recurrent Wavelet Neural Network Based on Immune Evolving Algorithm and its Application in Nonlinear Modeling

2014 ◽  
Vol 1049-1050 ◽  
pp. 1666-1669
Author(s):  
Xiang Jie Luo ◽  
Cheng Kai Wei ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Nonlinear Modeling ◽  
Wavelet Neural Network ◽  
Chaotic Time Series ◽  
Good Prediction ◽  
Training Algorithm ◽  
Connection Weight ◽  
Scaling Parameter ◽  
Prediction Capability

To improve the modeling performance of Recurrent Wavelet Neural Network (RWNN), a training algorithm based on Immune Evolving Algorithm (IEA) is proposed. In the process of RWNN training, IEA is mainly used to optimize the connection weight, translating and scaling parameter. The experiment result on Duffing chaotic time series shows that the proposed RWNN training algorithm has a good prediction capability in the field of nonlinear modeling.

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