Correntropy-based kernel learning for nonlinear system identification with unknown noise: an industrial case study

2013 ◽  
Vol 46 (32) ◽  
pp. 361-366 ◽  
Author(s):  
Yi Liu ◽  
Junghui Chen
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Kernel Learning ◽  
Industrial Case Study

NONLINEAR SYSTEM IDENTIFICATION BASED ON INTERNAL RECURRENT NEURAL NETWORKS

2009 ◽  
Vol 19 (02) ◽  
pp. 115-125 ◽  
Author(s):  
GHEORGHE PUSCASU ◽  
BOGDAN CODRES ◽  
ALEXANDRU STANCU ◽  
GABRIEL MURARIU
Keyword(s):  
Neural Networks ◽  
System Identification ◽  
Nonlinear System ◽  
Recurrent Neural Networks ◽  
Internal State ◽  
Nonlinear System Identification ◽  
Backpropagation Algorithm ◽  
Novel Approach ◽  
System States

A novel approach for nonlinear complex system identification based on internal recurrent neural networks (IRNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This approach employs internal state estimation when no measurements coming from the sensors are available for the system states. A modified backpropagation algorithm is introduced in order to train the IRNN for nonlinear system identification. The performance of the proposed design approach is proven on a car simulator case study.

scholarly journals Nonlinear System Identification with Dominating Output Noise - A Case Study on the Silverbox

2021 ◽  
Vol 54 (7) ◽  
pp. 679-684
Author(s):  
J. Schoukens ◽  
D. Westwick ◽  
L. Ljung ◽  
T. Dobrowiecki
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Output Noise
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