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A stable gradient algorithm of adaptation using an output signal
Author(s) -
Nikiforov V. O.
Publication year - 1992
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.4480060315
Subject(s) - adaptation (eye) , algorithm , signal (programming language) , scalar (mathematics) , control theory (sociology) , stability (learning theory) , computer science , mathematics , artificial intelligence , physics , geometry , control (management) , machine learning , optics , programming language
This paper studies the adaptation law design problem for a linear error model with a scalar output signal. The proposed gradient algorithm of adaptation allows one to provide asymptotic stability of a closed adaptive system without the necessity of error model positivity. The basis of the design is similar to that of Monopoli, who proposed using an additional signal proportional to the speed of parameter adjustment. Relationships of the algorithm and previous ones (the MIT‐rule and Narendra's et al. one) and its employment for a time‐variant error model are briefly discussed.