Polynomial NARX Model Identification: a Wiener–Hammerstein Benchmark

2009 ◽  
Vol 42 (10) ◽  
pp. 1074-1079 ◽  
Author(s):  
Luigi Piroddi ◽  
Marcello Farina ◽  
Marco Lovera
Keyword(s):  
Model Identification ◽  
Narx Model

scholarly journals NARX model identification with error filtering

2008 ◽  
Vol 41 (2) ◽  
pp. 2726-2731 ◽  
Author(s):  
Luigi Piroddi ◽  
Marco Lovera
Keyword(s):  
Model Identification ◽  
Narx Model ◽  
Error Filtering

scholarly journals Dynamic model identification of IPMC actuator using fuzzy NARX model optimized by MPSO

2014 ◽  
Vol 17 (1) ◽  
pp. 62-80
Author(s):  
Anh Pham Huy Ho ◽  
Nam Thanh Nguyen
Keyword(s):  
Dynamic Model ◽  
Model Identification ◽  
Training Data ◽  
The Novel ◽  
Inverse Dynamic ◽  
Modified Particle Swarm Optimization ◽  
Highly Nonlinear ◽  
Actuator System ◽  
Force Variation ◽  
Narx Model

In this paper, a novel inverse dynamic fuzzy NARX model is used for modeling and identifying the IPMC-based actuator’s inverse dynamic model. The contact force variation and highly nonlinear cross effect of the IPMC-based actuator are thoroughly modeled based on the inverse fuzzy NARX model-based identification process using experiment input-output training data. This paper proposes the novel use of a modified particle swarm optimization (MPSO) to generate the inverse fuzzy NARX (IFN) model for a highly nonlinear IPMC actuator system. The results show that the novel inverse dynamic fuzzy NARX model trained by MPSO algorithm yields outstanding performance and perfect accuracy.

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