outlier robust stochastic approximation algorithm for identification of mimo hammerstein models (Nonlinear Dynamics)
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Nonlinear Dynamics
outlier robust stochastic approximation algorithm for identification of mimo hammerstein models
Outlier Robust Stochastic Approximation Algorithm For Identification Of Mimo Hammerstein Models, supplied by Nonlinear Dynamics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/outlier+robust+stochastic+approximation+algorithm+for+identification+of+mimo+hammerstein+models/outlier+robust+stochastic+approximation+algorithm+for+identification+of+mimo+hammerstein+models/10__1007_slash_s12555___022___0053___4-150-1-15
Average 90 stars, based on 1 article reviews
Outlier Robust Stochastic Approximation Algorithm For Identification Of Mimo Hammerstein Models, supplied by Nonlinear Dynamics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/outlier+robust+stochastic+approximation+algorithm+for+identification+of+mimo+hammerstein+models/outlier+robust+stochastic+approximation+algorithm+for+identification+of+mimo+hammerstein+models/10__1007_slash_s12555___022___0053___4-150-1-15
Average 90 stars, based on 1 article reviews
outlier robust stochastic approximation algorithm for identification of mimo hammerstein models - by Bioz Stars,
2026-10
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other:Article Title: Parsimonious Model Based Consistent Subspace Identification of Hammerstein Systems Under Periodic Disturbances Article Snippet: The existing results show the applicability of the over-parameterized model based subspace identification method (OPM-like SIM) developed for consistent estimates of Hammerstein systems under completely unknown periodic disturbances.. However, it requires to estimate extra parameters and performer a low rank approximation step.. Therefore, it may give rise to unnecessarily high variance in parameter estimates, especially using a small and noisy data set. |