Paper Info
Title
Rapid system identification for jump Markov non-linear systems
Abstract
This work evaluates a previously introduced algorithm called Particle-Based Rapid Incremental Smoother within the framework of state inference and parameter identification in Jump Markov Non-Linear System. It is applied to the recursive form of two well-known Maximum Likelihood based algorithms who face the common challenge of online computation of smoothed additive functionals in order to accomplish the task of model parameter estimation. This work extends our previous contributions on identification of Markovian switching systems with the goal to reduce the computational complexity. A benchmark problem is used to illustrate the results.
Year
DOI
Venue
2017
10.1109/CAMSAP.2017.8313089
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Keywords
Field
DocType
jump Markov nonlinear systems,state inference,parameter identification,rapid system identification,particle-based rapid incremental smoother,maximum likelihood based algorithms,model parameter estimation,recursive form
Markov process,Nonlinear system,Computer science,Inference,Markov chain,Algorithm,Jump,System identification,Recursion,Computational complexity theory
Conference
ISBN
Citations 
PageRank 
978-1-5386-1252-1
0
0.34
References 
Authors
7
4
Name
Order
Citations
PageRank
A. R. Braga111.12
Carsten Fritsche215714.72
Fredrik Gustafsson32287281.33
Marcelo G. S. Bruno46011.99