Title :
Statistical Linearization Based on the Maximal Correlation
Author :
Chernyshov, K.R.
Author_Institution :
V.A. Trapeznikov Inst. of Control Sci., Moscow
Abstract :
The paper presents an approach to the statistical linearization of the input/output mapping of a non-linear discrete-time stochastic system driven by a white-noise Gaussian process. The approach is based on applying the maximal correlation function. At that, the statistical linearization criterion is the condition of coincidence of the mathematical expectations of the output processes of the system and model, and the condition of coincidence of the joint maximal correlation functions of the output and input processes of the system and the output and input processes of the model. Explicit expressions for the weight function coefficients of the linearized model are obtained.
Keywords :
Gaussian noise; correlation methods; discrete time systems; identification; nonlinear systems; stochastic systems; white noise; input-output mapping; maximal correlation; nonlinear discrete-time stochastic system; statistical linearization criterion; white-noise Gaussian process; Communication system control; Gaussian processes; Kernel; Mathematical model; Nonlinear systems; Random processes; Random variables; Stochastic processes; Stochastic systems; System identification; System identification; maximal correlation; measures of dependence; nonlinear system;
Conference_Titel :
Control and Communications, 2007. SIBCON '07. Siberian Conference on
Conference_Location :
Tomsk
Print_ISBN :
1-4244-0346-4
DOI :
10.1109/SIBCON.2007.371295
