PARTICLE FILTERING APPROACH TO STATE ESTIMATION IN BOOLEAN DYNAMICAL SYSTEMS Conference Paper

abstract

• Exact optimal state estimation for discrete-time Boolean dynamical systems may become impractical computationally if system dimensionality is large. In this paper, we consider a particle filtering approach to address this problem. The methodology is illustrated through application to state tracking in high-dimensional Boolean network models. The results show that the particle filter can be very accurate under a moderate number of particles. The impact of resampling on performance is also investigated. 2013 IEEE.

name of conference

• 2013 IEEE Global Conference on Signal and Information Processing

authors

• Braga Neto, Ulisses

published proceedings

• 2013 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP)

author list (cited authors)

• Braga-Neto, U.

citation count

• 5

complete list of authors

• Braga-Neto, Ulisses

publication date

• January 2010

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)  Journal

keywords

• Boolean Dynamical Systems
• Boolean Networks
• Optimal State Estimation
• Particle Filtering
• Sequential Monte-carlo Methods

Digital Object Identifier (DOI)

• 10.1109/globalsip.2013.6736818

International Standard Book Number (ISBN) 13

• 9781479902484

start page

• 81

end page

• 84