Identification method of neuro-fuzzy-based Hammerstein model with coloured noise

In this study, neuro-fuzzy-based identification procedure for Hammerstein model with coloured noise is presented. Separable signal is used to realise the decoupling of the identification of dynamic linear part from that of static non-linear part, and then correlation analysis method is adopted to id...

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Vydáno v:IET control theory & applications Ročník 11; číslo 17; s. 3026 - 3037
Hlavní autoři: Li, Feng, Li, Jia, Peng, Daogang
Médium: Journal Article
Jazyk:angličtina
Vydáno: The Institution of Engineering and Technology 24.11.2017
Témata:
coloured noise
convergence analysis
correlation analysis method
dynamic linear part
fuzzy control
fuzzy neural nets
gradient methods
gradient search theory
identification
identification method
linear systems
martingale theory
multiinnovation‐based extended stochastic gradient approach
neurocontrollers
neuro‐fuzzy‐based Hammerstein model
neuro‐fuzzy‐based identification procedure
nonlinear control systems
parameter estimation
parameter estimation error
persistent excitation condition
Research Article
search problems
static nonlinear part
stochastic processes
fuzzy neural nets
martingale theory
correlation analysis method
linear systems
neuro-fuzzy-based Hammerstein model
static nonlinear part
fuzzy control
identification method
identification
dynamic linear part
neuro-fuzzy-based identification procedure
convergence analysis
neurocontrollers
nonlinear control systems
gradient search theory
coloured noise
parameter estimation
stochastic processes
persistent excitation condition
gradient methods
multiinnovation-based extended stochastic gradient approach
search problems
parameter estimation error
ISSN:1751-8644, 1751-8652
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Popis
Shrnutí:In this study, neuro-fuzzy-based identification procedure for Hammerstein model with coloured noise is presented. Separable signal is used to realise the decoupling of the identification of dynamic linear part from that of static non-linear part, and then correlation analysis method is adopted to identify the parameters of the linear part. Furthermore, by combining multi-innovation and gradient search theory, multi-innovation-based extended stochastic gradient approach is derived for improving the parameters estimation accuracy of the non-linear part and the noise model. In addition, the convergence analysis in the martingale theory illustrates that the parameter estimation error will converge to zero under the persistent excitation condition. Finally, two simulation results demonstrate that the proposed approach has high identification accuracy and good robustness to the disturbance of coloured noise.
ISSN:1751-8644
1751-8652
DOI:10.1049/iet-cta.2017.0306