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

R Rakkiyappan¹, K Maheswari², G Velmurugan¹

¹Department of Mathematics, Bharathiar University, Coimbatore 641046, India.

Neural Networks : the Official Journal of the International Neural Network Society

|June 6, 2018

Summary

This study introduces an event-triggered communication scheme and logarithmic quantization for H∞ state estimation in semi-Markovian jumping neural networks, enhancing efficiency and conserving resources.

Keywords:

control Event-trigger scheme Exponential stability Quantization Semi-Markov jump neural networks

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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.