A Simulated Maximum Likelihood Method for Estimation of Stochastic Wiener Systems
2016 (English)Conference paper (Refereed)
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
Research subject Electrical Engineering
IdentifiersURN: urn:nbn:se:kth:diva-186218OAI: oai:DiVA.org:kth-186218DiVA: diva2:950982
55th IEEE Conference on Decision and Control (CDC 2016)