Author:
Kirill Chernyshov
Affiliation:
V. A. Trapeznikov Institute of Control Sciences, Russian Federation
Keyword(s):
Cauchy-Schwarz Divergence, Input/Output Model, Maximal Correlation, Measures of Dependence, Mutual Information, Rényi Entropy, Statistical Linearization, System Identification.
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:
The paper presents a unified approach to the statistical linearization of input/output mapping of non-linear
discrete-time stochastic systems driven with white-noise Gaussian process. The approach is concerned with
a possibility of applying any consistent measures of dependence (that is those measures of dependence of a
pair of random values, which vanish if and only if these random values are stochastically independent) in
statistical linearization problems and oriented to the elimination of drawbacks concerned with applying correlation
and dispersion (based on the correlation ratio) measures of dependence, based on linearized representations
of their input/output models.