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Related Experiment Video

Updated: Mar 9, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Asynchronous Dissipative State Estimation for Stochastic Complex Networks With Quantized Jumping Coupling and

Yong Xu, Renquan Lu, Hui Peng

IEEE Transactions on Neural Networks and Learning Systems

|January 6, 2017

Summary

This summary is machine-generated.

This study presents a new asynchronous state estimator for complex networks with random coupling and uncertain measurements. The method ensures stability and dissipativity for improved network performance.

Related Experiment Videos

Last Updated: Mar 9, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

Control Systems Engineering
Network Science
Stochastic Systems

Background:

State estimation is crucial for complex networks.
Challenges include random coupling and measurement uncertainty.
Existing methods struggle with asynchronous information and constraints.

Purpose of the Study:

To develop an asynchronous state estimator for discrete-time stochastic complex networks.
To address randomly varying coupling governed by a Markov chain.
To handle capacity constraints and multiplicative measurement noise.

Main Methods:

Designed an asynchronous estimator overcoming information access limitations.
Utilized a logarithmic quantizer for capacity constraints.
Employed multiplicative noise to model measurement uncertainty.
Augmented the estimation error system using the Kronecker product.

Main Results:

Established sufficient conditions for stochastic stability of the estimation error system.
Guaranteed strict (Q, S, R)-γ-dissipativity.
Derived estimator gains via the linear matrix inequality method.

Conclusions:

The proposed asynchronous estimator is effective for complex networks.
The design techniques ensure system stability and performance.
Numerical examples validate the new approach.