Optimal state estimation for Boolean dynamical systems

Author

Braga-Neto, Ulisses

Author_Institution

Dept. of Electr. Eng., Texas A & M Univ., College Station, TX, USA

fYear

2011

fDate

6-9 Nov. 2011

Firstpage

1050

Lastpage

1054

Abstract

A novel state-space signal model is proposed for discrete-time Boolean dynamical systems. State evolution is governed by Boolean functions (i.e., logic gates) plus binary noise. The current system state is observed through an arbitrary function plus observation noise. The optimal recursive MMSE estimator for this model is called the Boolean Kalman filter (BKF), and an efficient algorithm is presented for its exact computation. The BKF is illustrated through an example of optimal context inference for Probabilistic Boolean Networks.

Keywords

Boolean functions; Kalman filters; mean square error methods; probability; recursive estimation; signal processing; state estimation; BKF; Boolean Kalman filter; Boolean function; arbitrary function; binary noise; discrete-time Boolean dynamical system; observation noise; optimal context inference; optimal recursive MMSE estimator; optimal state estimation; probabilistic Boolean network; state evolution; state-space signal model; Computational modeling; Context; Hidden Markov models; Kalman filters; Mathematical model; Noise; Vectors; Boolean network with perturbation; CoD estimation; maximum liklihood estimation; stochastic logic model;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers (ASILOMAR), 2011 Conference Record of the Forty Fifth Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

978-1-4673-0321-7

Type

conf

DOI

10.1109/ACSSC.2011.6190172

Filename

6190172