Event-triggered H_∞ state estimation for semi-Markov jumping discrete-time neural networks with quantization

Area of Science:

• Control Systems Engineering
• Networked Systems
• Artificial Neural Networks

Background:

• State estimation is crucial for monitoring and controlling complex systems.
• Semi-Markovian jumping neural networks introduce time-varying dynamics.
• Limited communication resources necessitate efficient data transmission strategies.

Purpose of the Study:

• To develop an H∞ state estimation method for semi-Markovian jumping discrete-time neural networks.
• To implement an event-triggered scheme to reduce communication load.
• To utilize quantization to further improve network efficiency.

Main Methods:

• An event-triggered communication scheme determines data transmission based on specific criteria.
• A logarithmic quantizer reduces data transmission rates.
• Linear matrix inequalities (LMIs) are used to derive a stabilization criterion.

Main Results:

• The proposed event-triggered scheme conserves communication resources.
• The logarithmic quantizer enhances network communication efficiency.
• A stabilization criterion guarantees the H∞ performance of the estimation error system.

Conclusions:

• The integrated approach of event-triggered communication and quantization effectively addresses H∞ state estimation challenges.
• The derived LMIs provide a robust method for ensuring system performance.
• Numerical simulations validate the proposed scheme's effectiveness.