Estimating uncertain delayed genetic regulatory networks: An adaptive filtering approach

Uncertain delayed genetic regulatory networks are investigated from an adaptive filtering approach based on an adaptive synchronization setting. For an unknown regulatory network with time delay and uncertain noise disturbance, several adaptive laws are derived to ensure the stochastic stability of the error states between the unknown network and the estimated model. The novelty lies in the fact that the designed adaptive laws are independent of the unknown system states and parameters, requiring only the output and structure of the underlying network. A representative simulation example is given to verify the effectiveness of the theoretical results.