Author:
Grzegorz Mzyk
Affiliation:
Department of Electronics, Wrocaw University of Science and Technology, W. Wyspiaskiego 27, 50-370 Wrocaw and Poland
Keyword(s):
System Identification, Nonparametric Estimation, Kernel Regression, Orthogonal Expansion, Hammerstein System, Wiener System.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Nonlinear Signals and Systems
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Identification
;
System Modeling
Abstract:
In the paper the first version of Nonparametric System Identification Matlab Toolbox is presented. It is based on theoretical results concerning nonparametric identification method, achieved for the last four decades. The library includes both standard (kernel based or orthogonal expansion based) nonparametric methods and recent algorithms including combined (parametric-nonparametric) algorithms. Hammerstein and Wiener models and their serial connections are considered. Nonparametric estimates, usually run as a preliminary steps, play supporting role in the main procedure of estimating system parameters by the least squares method. Multi-level (hybrid) structure of algorithms, i.e. combining both parametric and nonparametric approaches allows to decompose the problem of identification of interconnected complex system into simpler local subproblems. Moreover, asymptotic consistency of all estimates was formally proved, even under existence of random and correlated noise.