Robust nonlinear system identification using neural network models

2002 ◽  
Author(s):  
Sonwu Lu ◽  
T. Basar
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Network Models ◽  
Neural Network Models

Computational cost improvement of neural network models in black box nonlinear system identification

2015 ◽  
Vol 166 ◽  
pp. 96-108 ◽  
Author(s):  
Hector M. Romero Ugalde ◽  
Jean-Claude Carmona ◽  
Juan Reyes-Reyes ◽  
Victor M. Alvarado ◽  
Juan Mantilla
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Computational Cost ◽  
Network Models ◽  
Black Box ◽  
Neural Network Models

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.

scholarly journals Efficient hinging hyperplanes neural network and its application in nonlinear system identification

Automatica ◽  
2020 ◽  
Vol 116 ◽  
pp. 108906 ◽  
Author(s):  
Jun Xu ◽  
Qinghua Tao ◽  
Zhen Li ◽  
Xiangming Xi ◽  
Johan A.K. Suykens ◽  
...  
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification
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