A Recursive Approach to Quantized H_∞ State Estimation for Genetic Regulatory Networks Under Stochastic Communication...

Area of Science:

• Systems Biology
• Control Theory
• Network Science

Background:

• Genetic regulatory networks (GRNs) are complex biological systems crucial for cellular function.
• High-throughput sequencing generates large datasets, necessitating efficient data transmission for analysis.
• Limited bandwidth and quantization effects pose challenges in remote state estimation for GRNs.

Purpose of the Study:

• To design a finite-horizon quantized H∞ state estimator for discrete time-varying GRNs.
• To address challenges posed by quantization and stochastic communication protocols (SCPs).
• To ensure the state estimation error dynamics meet a prescribed H∞ performance requirement.

Main Methods:

• Utilizing the completing-the-square technique to derive sufficient conditions.
• Designing a time-varying state estimator.
• Solving coupled backward recursive Riccati difference equations for estimator parameters.
• Implementing stochastic communication protocols (SCPs) for data transmission scheduling.

Main Results:

• Sufficient conditions are derived to guarantee the H∞ estimation performance.
• A novel time-varying state estimator is designed for the specified GRN model.
• The effectiveness of the proposed estimator is demonstrated through a numerical example.

Conclusions:

• The proposed state estimation method effectively handles quantization and SCPs in GRNs.
• The designed estimator ensures finite-horizon H∞ performance for discrete time-varying GRNs.
• This work contributes to robust state estimation in complex biological networks under communication constraints.