Name
Abstract | ||
|---|---|---|
This article studies the state estimation for probabilistic Boolean networks via observing output sequences. Detectability describes the ability of an observer to uniquely estimate system states. By defining the probability of an observed output sequence, a new concept called detectability measure is proposed. The detectability measure is defined as the limit of the sum of probabilities of all detectable output sequences when the length of output sequences goes to infinity, and it can be regarded as a quantitative assessment of state estimation. A stochastic state estimator is designed by defining a corresponding nondeterministic stochastic finite automaton, which combines the information of state estimation and probability of output sequences. The proposed concept of detectability measure further performs the quantitative analysis on detectability. Furthermore, by defining a Markov chain, the calculation of detectability measure is converted to the calculation of the sum of probabilities of certain specific states in Markov chain. Finally, numerical examples are given to illustrate the obtained theoretical results. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TNNLS.2021.3059795 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | DocType | Volume |
Detectability measure,Markov chain,output sequences,probabilistic Boolean networks (PBNs),state estimation | Journal | 33 |
Issue | ISSN | Citations |
9 | 2162-237X | 0 |
PageRank | References | Authors |
0.34 | 34 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Zhong | 1 | 171 | 14.53 |
| Zongxi Yu | 2 | 0 | 0.34 |
| Yuanyuan Li | 3 | 148 | 21.33 |
| Jianquan Lu | 4 | 2337 | 116.05 |