Optimal State Estimation of Boolean Control Networks under Stochastic Function Perturbations
Abstract
Abstract. This paper studies the state estimation (SE) problem of Boolean control networks (BCNs) with stochastic function perturbations (FPs). Initially, we introduce three distinct types of FP that influence BCNs, transforming them into a stochastic system. Subsequently, we outline the general optimal estimation problem for BCNs in the presence of FPs. We propose an iterative method, based on the Kalman filter framework, to compute the probability of possible states given known input and output sequences. Furthermore, we treat probabilistic BCNs (PBCNs) as an extension of multibit FPs and present an SE approach for PBCNs. The system’s minimum conditional mean-square error is determined based on the results of the iterative process. To validate the theoretical results, we provide two numerical examples that demonstrate the effectiveness of the proposed methods.
Fields of Study & Subjects
Gene Regulatory Network Analysis
Formal Methods in Verification
Probabilistic and Robust Engineering Design
Publication details
- Journal / source
- SIAM Journal on Control and Optimization
- Publisher
- Society for Industrial & Applied Mathematics (SIAM)
- Volume
- 64
- Issue
- 1
- Pages
- 450–471
- Publication date
- 5 February 2026
- Language
- EN