Title :
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
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;
Conference_Titel :
Signals, Systems and Computers (ASILOMAR), 2011 Conference Record of the Forty Fifth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
978-1-4673-0321-7
DOI :
10.1109/ACSSC.2011.6190172
