State estimation for delayed genetic regulatory networks with reaction diffusion terms and Markovian jump
Chengye Zou, Changjun Zhou, Qiang Zhang, Xinyu He, Huang Chun
Abstract
Abstract Robust state estimation for delayed genetic regulatory networks with reaction–diffusion terms and uncertainties terms under Dirichlet boundary conditions is addressed in this article. The main purpose of the problem investigation is to design a novel state observer for estimate the true concentrations of mRNA and protein by available measurement outputs. Based on Lyapunov–Krasovskii functions and linear matrix inequalities (LMI), sufficient conditions are given to ensure the robust stability of the estimation error networks. Two examples are presented to illustrate the effectiveness of the proposed approach.
Topics & Concepts
Computational intelligenceState (computer science)Control theory (sociology)Stability (learning theory)MathematicsObserver (physics)EstimationDirichlet distributionReaction–diffusion systemMathematical optimizationComputer scienceApplied mathematicsBoundary value problemAlgorithmEngineeringMathematical analysisPhysicsArtificial intelligenceControl (management)Machine learningSystems engineeringQuantum mechanicsGene Regulatory Network AnalysisGene expression and cancer classificationBioinformatics and Genomic Networks