14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper WeB1.6

Juloski, Aleksandar (Eindhoven Univ. of Tech.), Weiland, Siep (Eindhoven Univ. of Tech.)

A Bayesian Approach to the Identification of Piecewise Linear Output Error Models

Scheduled for presentation during the Regular Session "Identification of Hybrid and Parameter Varying Systems" (WeB1), Wednesday, March 29, 2006, 17:10−17:30, Concert Hall

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 22, 2018

Keywords Hybrid and Distributed System Identification, Bayesian Methods, Particle Filtering/Monte Carlo Methods


In this paper we develop an algorithm for the identification of piecewise linear output error models for the case where the discrete mode of the underlying hybrid system is not known. The presented algorithm is based on a Bayesian framework, i.e. unknown model parameters are treated as random variables and described with probability density functions. The identification problem is posed as a problem of computing the posterior parameter densities, given the prior densities and the observed data. A suboptimal identification algorithm is derived. Operation of the algorithm is demonstrated on an example.