Nonlinear System Identification using Genetic Algorithm Based Recurrent Neural Networks

2006 ◽  
Author(s):  
Yu-qing Zhu ◽  
Wen-fang Xie ◽  
Jie Yao
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
System Identification ◽  
Nonlinear System ◽  
Recurrent Neural Networks ◽  
Nonlinear System Identification

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.

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