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ITERATIVE AND RECURSIVE ESTIMATION OF TRANSFER FUNCTIONS
Author(s) -
Grillenzoni Carlo
Publication year - 1991
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1991.tb00072.x
Subject(s) - mathematics , estimator , consistency (knowledge bases) , stochastic approximation , convergence (economics) , mathematical optimization , strong consistency , regression , least squares function approximation , computer science , statistics , geometry , computer security , key (lock) , economics , economic growth
. A unified treatment of non‐linear estimation, pseudolinear regression and stochastic approximation for open‐loop transfer function models is provided. Pseudolinear regression techniques are used to derive the recursive non‐linear least‐squares estimator, avoiding the methodological problems implicit in traditional derivations. Stochastic approximation analysis is used to investigate in a direct manner the conditions of convergence and consistency of both iterative and recursive algorithms. The various methods are compared using data for an industrial process.