Robust filtering for gene expression time series data with variance constraints
International Journal of Computer Mathematics - Bioinformatics
Robust H2 control of Markovian jump systems with uncertain switching probabilities
International Journal of Systems Science
IEEE Transactions on Neural Networks
On the transient and steady-state estimates of interval genetic regulatory networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
State estimation for delayed neural networks
IEEE Transactions on Neural Networks
Exponential Stability of Discrete-Time Genetic Regulatory Networks With Delays
IEEE Transactions on Neural Networks
l2-l∞ filter design for discrete-time singular Markovian jump systems with time-varying delays
Information Sciences: an International Journal
| Hi-index | 0.00 |
In this article, the state estimation problem is investigated for genetic regulatory networks (GRNs) with parameter uncertainties and stochastic disturbances. To account for the unavoidable modelling errors and parameter fluctuations, the network parameters are assumed to be time-varying but norm-bounded. Furthermore, scalar multiplicative white noises are introduced into both the translation process and the feedback regulation process in order to reflect the inherent intracellular and extracellular noise perturbations. The purpose of the addressed problem is to design a linear state estimator that can estimate the true concentration of the mRNA and the protein of the uncertain GRNs. By resorting to the Lyapunov-Krasovskii functional method combined with the linear matrix inequality (LMI) technique, sufficient conditions are first established for ensuring the stochastic stability of the dynamics of the estimation error, and the estimator gains are then designed in terms of the solutions to some LMIs that can be easily solved by using the standard numerical software. A three-node GRN is presented to show the effectiveness of the proposed design procedures.