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Kalman Filtering for Genetic Regulatory Networks with Missing Values
Ist Teil von
Computational and mathematical methods in medicine, 2017-01, Vol.2017, p.7837109-9
Ort / Verlag
United States: Hindawi
Erscheinungsjahr
2017
Link zum Volltext
Quelle
EZB Electronic Journals Library
Beschreibungen/Notizen
The filter problem with missing value for genetic regulation networks (GRNs) is addressed, in which the noises exist in both the state dynamics and measurement equations; furthermore, the correlation between process noise and measurement noise is also taken into consideration. In order to deal with the filter problem, a class of discrete-time GRNs with missing value, noise correlation, and time delays is established. Then a new observation model is proposed to decrease the adverse effect caused by the missing value and to decouple the correlation between process noise and measurement noise in theory. Finally, a Kalman filtering is used to estimate the states of GRNs. Meanwhile, a typical example is provided to verify the effectiveness of the proposed method, and it turns out to be the case that the concentrations of mRNA and protein could be estimated accurately.