Improved Initialization for Nonlinear State-Space Modeling
Artikel i vetenskaplig tidskrift, 2014

This paper discusses a novel initialization algorithm for the estimation of nonlinear state-space models. Good initial values for the model parameters are obtained by identifying separately the linear dynamics and the nonlinear terms in the model. In particular, the nonlinear dynamic problem is transformed into an approximate static formulation, and simple regression methods are applied to obtain the solution in a fast and efficient way. The proposed method is validated by means of two measurement examples: the Wiener-Hammerstein benchmark problem, and the identification of a crystal detector.

system identification

nonlinear modeling

state-space models

nonlinear dynamical systems

Multilayer perceptrons


A. Marconato

Vrije Universiteit Brussel

Jonas Sjöberg

Signaler och system, System- och reglerteknik, Mekatronik

J. Suykens

KU Leuven

J. Schoukens

Vrije Universiteit Brussel

IEEE Transactions on Instrumentation and Measurement

0018-9456 (ISSN)

Vol. 63 972-980