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Related Experiment Video

Updated: Nov 30, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Stability Analysis for Delayed Neural Networks via a Novel Negative-Definiteness Determination Method.

Fei Long, Chuan-Ke Zhang, Yong He

IEEE Transactions on Cybernetics

|November 17, 2020

Summary

This study enhances neural network stability analysis using a novel Lyapunov-Krasovskii functional and a less conservative negative-definiteness determination method for time-varying delays.

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Published on: December 7, 2021

Area of Science:

Control Theory
Artificial Intelligence
Dynamical Systems

Background:

Neural networks with time-varying delays present significant stability analysis challenges.
Existing methods for stability analysis often involve conservatism and strict conditions.

Purpose of the Study:

To develop a more effective stability analysis method for neural networks with time-varying delays.
To introduce a relaxed Lyapunov-Krasovskii functional (LKF) and a novel negative-definiteness determination approach.

Main Methods:

A relaxed Lyapunov-Krasovskii functional (LKF) is proposed, freeing positive-definiteness requirements and augmenting integral terms.
A new negative-definiteness determination method for quadratic functions is introduced, utilizing Taylor's formula and interval decomposition.
The new method is less conservative than previous approaches.

Main Results:

The proposed LKF and negative-definiteness determination method are applied to stability analysis of neural networks with time-varying delays.
The effectiveness and advantages of the proposed methods are demonstrated through two numerical examples.

Conclusions:

The developed approach offers improved stability analysis for neural networks with time-varying delays.
The novel methods reduce conservatism, leading to potentially broader applicability in neural network design and analysis.