An Approach of Bayesian Filtering for Stochastic Boolean Dynamic Systems

作者：Ma, Hongbin^*; Wang, Dong; Qi, Hongsheng; Fu, Mengyin

来源：26th Chinese Control and Decision Conference (CCDC), Changsha, PEOPLES R CHINA, 2014-05-31 To 2014-06-02.

• Stochastic Boolean dynamic systems
• Bayesian filtering
• estimation algorithm
• estimation error rate

This paper introduces an approach to estimate the true states for stochastic Boolean dynamic system (SBDS), where the state evolution is governed by Boolean functions with additive binary process noise while the measurement is an arbitrary function of the state yet with additive binary measurement noise. The problem of figuring out the true state using the only available noisy outputs is crucial for practical applications of Boolean dynamic system models, however, for such Boolean systems with wide background, there are no ready-to-use convenient tools like Kahnan filter for linear systems. To resolve this challenging problem, an approach based on Bayesian filtering called Boolean Bayesian Filter (BBF) is put forward to estimate the true states of SBDS, and an efficient algorithm is presented for their exact computation. An index to evaluate the filtering performance, named estimation error rate, is put forward in this paper as well. In addition, extensive simulations via actual examples have illustrated the effectiveness of the proposed algorithm based on BBF.

• 出版日期2014
• 单位复杂系统智能控制与决策国家重点实验室; 中国科学院数学与系统科学研究院; 北京理工大学