Title :
Mode-dependent state estimation for discrete-time genetic regulatory networks with a random delay described by a Markovian chain
Author :
Weijun Ma ; Shimo Wang ; Yantao Wang
Author_Institution :
Sch. of Math. Sci., Heilongjiang Univ., Harbin, China
Abstract :
This paper deals with the robust state estimation problem for a class of discrete-time genetic regulatory networks (GRNs) with a random delay described by a Markovian chain. The norm-bounded uncertainties and random delay described by a Markovian chain are considered in the discrete-time GRNs. Based on the Lyapunov stability theory and matrix inequality technique, sufficient conditions are derived to ensure the error state system to be (robustly) stochastically stable in the mean square sense. Numerical examples are given to show the effectiveness of the developed results.
Keywords :
Lyapunov methods; Markov processes; delay systems; discrete time systems; genetics; matrix algebra; mean square error methods; state estimation; stochastic systems; uncertain systems; Lyapunov stability theory; Markovian chain; discrete-time GRN; discrete-time genetic regulatory network; error state system; matrix inequality technique; mean square sense; mode-dependent state estimation; norm-bounded uncertainty; random delay; robust state estimation problem; robustly stochastically stable; sufficient condition; Delay effects; Delays; Linear matrix inequalities; Proteins; State estimation; Symmetric matrices; Uncertainty;
Conference_Titel :
Mechatronics and Control (ICMC), 2014 International Conference on
Print_ISBN :
978-1-4799-2537-7
DOI :
10.1109/ICMC.2014.7231682
