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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

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Maintaining synchronization by decentralized feedback control in time delay neural networks with parameter

Mou Chen¹, Chang-Sheng Jiang, Qing-Xian Wu

¹Automation College, Nanjing University of Aeronautics and Astronautics, Yu Dao street 29, Nanjing, 210016, China. chenmou@nuaa.edu.cn

International Journal of Neural Systems

|June 15, 2007

Summary

This study introduces a decentralized control method to synchronize neural networks with time delays and uncertainties. The proposed controller ensures reliable synchronization despite system disturbances.

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

Neuroscience
Control Theory
Systems Engineering

Background:

Neural networks are crucial for complex computations.
Synchronization of coupled systems is vital in various applications.
Time delays and parameter uncertainties pose significant challenges in control systems.

Purpose of the Study:

To propose a decentralized feedback control scheme for synchronizing linearly coupled identical neural networks.
To address the challenges posed by time-varying delays and parameter uncertainties.
To develop a robust synchronization strategy for neural network systems.

Main Methods:

Investigating uncertain nonlinear synchronization error dynamics.
Utilizing linear matrix inequality (LMI) techniques for controller design.
Developing a sufficient condition for achieving synchronization.

Main Results:

A decentralized synchronization controller was designed.
The controller effectively drives synchronization error to zero.
The control scheme demonstrates robustness against system uncertainties and external disturbances.

Conclusions:

The proposed decentralized control scheme successfully synchronizes neural networks with time-varying delays and uncertainties.
LMI techniques provide an effective framework for designing robust synchronization controllers.
The method offers a reliable approach for achieving synchronization in complex neural network systems.