Variable knots spline approximation recursive Bayesian algorithm for identification of Wiener systems
JING Shao-xue
LI Zheng-ming
Abstract：To estimate the Wiener nonlinear systems with process noise, a recursive Bayesian algorithm based on cu-bic spline approximation is proposed. It's well known that the polynomial approximation does not extrapolate well and high degree polynomials have oscillatory behavior, etc. To overcome these drawbacks, a cubic spline function is used to approximate the inverse function of the output nonlinearity. And then the original Wiener system is parameterized to be a pseudo-linear regression model. The estimated variance of the noise is also integrated in the algorithm to estimate the pa-rameters. In order to approximate the inverse nonlinearity, a mean-value based variable knot-selection method is employed. After the convergence is analyzed, a numerical simulation and a case study validate the algorithm.
Keywords：parameter estimationWiener systemprocess noisecubic spline functionrecursive Bayesian algorithmvariable knots
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 13-21 )