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Zonotope-Based Distributed Set-Membership Fusion Estimation for Artificial Neural Networks Under the Dynamic

Zhongyi Zhao, Zidong Wang, Lei Zou

IEEE Transactions on Neural Networks and Learning Systems

|November 15, 2023

Summary

This summary is machine-generated.

This study introduces a distributed set-membership fusion estimation algorithm for artificial neural networks (ANNs) using a dynamic event-triggered mechanism (ETM). The method ensures bounded estimation errors, improving accuracy and performance while conserving resources.

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Area of Science:

Control Systems Engineering
Artificial Intelligence
Estimation Theory

Background:

Distributed estimation is crucial for complex systems like artificial neural networks (ANNs).
Event-triggered mechanisms (ETMs) optimize resource usage by reducing data transmission.
Set-membership estimation provides guaranteed bounds on estimation errors.

Purpose of the Study:

To design a distributed set-membership fusion estimation algorithm for ANNs.
To ensure the global estimation error is contained within a zonotope with a ultimately bounded radius.
To enhance estimation accuracy and performance while minimizing resource consumption.

Main Methods:

Utilizing zonotope properties and linear matrix inequality (LMI) techniques.
Developing a dynamic event-triggered mechanism (ETM) for sensor-to-estimator communication.
Applying order reduction techniques and solving bilinear matrix inequalities for estimator parameter derivation.

Main Results:

A zonotope for prediction error is calculated, improving prediction accuracy.
A zonotope for local estimation error is derived, enhancing estimation performance.
A sufficient condition is established for the ultimate boundedness of the local estimation error zonotope radius.

Conclusions:

The proposed zonotope-based distributed fusion estimator effectively minimizes the upper bound of the global estimation error radius.
The method demonstrates improved estimation performance and resource efficiency in ANNs.
Numerical examples validate the effectiveness of the developed distributed fusion estimation approach.