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State Estimation for Recurrent Neural Networks With Intermittent Transmission.

Chang Liu, Hongxia Rao, Xinxin Yu

IEEE Transactions on Cybernetics

|April 6, 2023

Summary

This summary is machine-generated.

This study enhances recurrent neural network state estimation over limited communication channels using an intermittent transmission protocol. It ensures system stability and performance under data constraints.

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A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

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Published on: May 25, 2019

Area of Science:

Control Systems Engineering
Machine Learning
Networked Systems

Background:

Recurrent neural networks (RNNs) are crucial for sequential data but face challenges in networked environments.
Capacity-constrained communication channels limit data transmission, impacting RNN state estimation accuracy.
Intermittent transmission protocols offer a solution to reduce communication load.

Purpose of the Study:

To develop a robust state estimation method for RNNs over unreliable communication channels.
To design an estimator resilient to intermittent data transmission.
To analyze and guarantee the stability and performance of the estimation error system.

Main Methods:

Design of a transmission interval-dependent estimator for RNNs.
Derivation of an estimation error system.
Proof of mean-square stability using an interval-dependent Lyapunov function.
Analysis of system performance across transmission intervals.

Main Results:

Establishment of sufficient conditions for mean-square stability of the estimation error system.
Derivation of conditions for strict (Q,S,R) - γ -dissipativity.
Demonstration of the estimator's correctness and superiority via a numerical example.

Conclusions:

The proposed interval-dependent estimator effectively addresses state estimation for RNNs under communication constraints.
The established conditions ensure robust stability and performance guarantees.
The methodology provides a valuable framework for networked control systems with limited bandwidth.