Nonlinear System Identification Based on Recurrent Wavelet Neural Network

2009 ◽  
pp. 517-525 ◽  
Author(s):  
Fengyao Zhao ◽  
Liangming Hu ◽  
Zongkun Li
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Wavelet Neural Network

A nonlinear system identification approach based on Fuzzy Wavelet Neural Network

2015 ◽  
Vol 28 (1) ◽  
pp. 225-235 ◽  
Author(s):  
Leandro L.S. Linhares ◽  
José M. Araújo Jr. ◽  
Fábio M.U. Araújo ◽  
Takashi Yoneyama
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Wavelet Neural Network ◽  
Identification Approach ◽  
Fuzzy Wavelet Neural Network

scholarly journals Fuzzy jump wavelet neural network based on rule induction for dynamic nonlinear system identification with real data applications

PLoS ONE ◽  
2019 ◽  
Vol 14 (12) ◽  
pp. e0224075
Author(s):  
Mohsen Kharazihai Isfahani ◽  
Maryam Zekri ◽  
Hamid Reza Marateb ◽  
Miguel Angel Mañanas
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Real Data ◽  
Rule Induction ◽  
Wavelet Neural Network

scholarly journals A Nonlinear System Identification Method Based on Adaptive Neural Network

2021 ◽  
Vol 28 (2) ◽  
pp. 111-123
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Generic Model ◽  
Control Engineering ◽  
The Neural Network ◽  
Adaptive Pso ◽  
And Control ◽  
Scientific Engineering

Nonlinear system identification (NSI) is of great significance to modern scientific engineering and control engineering. Despite their identification ability, the existing analysis methods for nonlinear systems have several limitations. The neural network (NN) can overcome some of these limitations in NSI, but fail to achieve desirable accuracy or training speed. This paper puts forward an NSI method based on adaptive NN, with the aim to further improve the convergence speed and accuracy of NN-based NSI. Specifically, a generic model-based nonlinear system identifier was constructed, which integrates the error feedback and correction of predictive control with the generic model theory. Next, the radial basis function (RBF) NN was optimized by adaptive particle swarm optimization (PSO), and used to build an NSI model. The effectiveness and speed of our model were verified through experiments. The research results provide a reference for applying the adaptive PSO-optimized RBFNN in other fields.

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