Journal
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
Volume 36, Issue 10, Pages 2562-2584Publisher
WILEY
DOI: 10.1002/acs.3471
Keywords
auxiliary model; bilinear-in-parameter system; fractional adaptive algorithms; hierarchical identification; parameter estimation
Funding
- National Natural Science Foundation of China [62003249, 62073250, 62173262]
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This study investigates the parameter identification issues of bilinear-in-parameter systems through fractional adaptive algorithms. The proposed algorithms, based on auxiliary model identification idea and convergence index, provide improved accuracy and computational efficiency for parameter estimation. Numerical simulations further verify the effectiveness and accuracy of the proposed algorithms.
This study investigates the parameter identification issues of bilinear-in-parameter systems through fractional adaptive algorithms. An auxiliary model based epsilon-normalized$$ \varepsilon \hbox{-} \mathrm{normalized} $$ modified fractional least mean square algorithm is proposed for accelerating the parameter estimation accuracy based on the auxiliary model identification idea and the introduced convergence index, a normalized modified hierarchical fractional least mean square algorithm is presented for improving the computational efficiency based on the hierarchical identification principle. The proposed normalized fractional adaptive strategies are effective and could provide more accurate parameter estimates comparing with conventional counterparts for bilinear-in-parameter identification model based on the mean square error metrics and the average predicted output error. The effectiveness and accuracy of the proposed algorithms are further verified and validated through numerical simulations for different noise variances, fractional orders and gain parameters.
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