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A Simulated Maximum Likelihood Method for Estimation of Stochastic Wiener Systems
KTH, School of Electrical Engineering (EES), Automatic Control. (System Identification)ORCID iD: 0000-0001-5474-7060
KTH, School of Electrical Engineering (EES), Automatic Control. (System Identification)
2016 (English)Conference paper (Refereed)
Abstract [en]

This paper introduces a simulation-based method for maximum likelihood estimation of stochastic Wienersystems. It is well known that the likelihood function ofthe observed outputs for the general class of stochasticWiener systems is analytically intractable. However, when the distributions of the process disturbance and the measurement noise are available, the likelihood can be approximated byrunning a Monte-Carlo simulation on the model. We suggest the use of Laplace importance sampling techniques for the likelihood approximation. The algorithm is tested on a simple first order linear example which is excited only by the process disturbance. Further, we demonstrate the algorithm on an FIR system with cubic nonlinearity. The performance of the algorithm is compared to the maximum likelihood method and other recent techniques.

Place, publisher, year, edition, pages
National Category
Signal Processing
Research subject
Electrical Engineering
URN: urn:nbn:se:kth:diva-186218OAI: diva2:950982
55th IEEE Conference on Decision and Control (CDC 2016)
Available from: 2016-05-04 Created: 2016-05-04 Last updated: 2016-08-04Bibliographically approved

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Abdalmoaty, MohamedHjalmarsson, Håkan
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