Particle Filtering Approach To State Estimation In Boolean Dynamical Systems
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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.
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