Parameter learning of delayed Boolean control networks with missing observations

Abstract

In this paper, a new and effective parameter learning method for delayed Boolean control networks (DBCNs) with missing observations is presented in this paper, which can handle strong noise and converge to the true parameter values in finite times. Two types of noise that follow different time-varying Bernoulli distributions are considered in this paper: measurement noise and system noise. The concept of virtual nodes is introduced by the paper to cope with random missing observations, and the algebraic representation of DBCNs is obtained using the STP method. A parameter learning algorithm based on the forward and backward probability and EM algorithm is then proposed by the paper, which can estimate the model parameters from time series data. The performance and robustness of the proposed method are demonstrated by the paper through a numerical example.