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Development of a novel robust identification scheme for nonlinear dynamic systems
Author(s) -
George Nithin V.,
Panda Ganapati
Publication year - 2015
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2482
Subject(s) - outlier , computer science , identification scheme , benchmark (surveying) , identification (biology) , nonlinear system , algorithm , robustness (evolution) , system identification , computational complexity theory , block (permutation group theory) , minification , mean squared error , artificial intelligence , mathematics , data mining , biochemistry , chemistry , botany , physics , geometry , geodesy , statistics , quantum mechanics , biology , gene , geography , programming language , measure (data warehouse)
Summary This paper presents a set of single layer low complexity nonlinear adaptive models for efficient identification of dynamic systems in the presence of outliers in the training signal. The weights of the new models have been updated using a new robust learning algorithm. The proposed robust algorithm is based on adaptive minimization of Wilcoxon norm of errors. The computational complexity associated with the new models has further been reduced by processing the input in block form and using a newly derived robust block learning algorithm. Through exhaustive simulation study of many benchmark identification examples, it has been shown that in all cases, the new models provide enhanced and robust identification performance compared with that provided by the corresponding conventional squared error‐based approaches. Copyright © 2014 John Wiley & Sons, Ltd.