State estimation of switched finite-field networks: A multi-valued particle filter approach

Abstract

This paper presents a novel and robust framework for state estimation of switched finite-field networks (SFFNs), which are a class of multi-valued logical networks that can model complex systems. The paper uses the semi-tensor product (STP) method to transform the dynamical model of SFFNs into an algebraic form, which can be easily manipulated and analyzed. The paper then applies a Monte-Carlo-based sequential importance sampling (SIS) filter to estimate the state of SFFNs from noisy and incomplete observations. The paper also introduces a resampling algorithm (RA) to prevent the particle degeneracy problem (PDP), which can affect the accuracy and efficiency of the filter. Finally, the paper demonstrates the performance and advantages of the proposed multi-valued particle filter (MVPF) framework through three examples.