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Predefined-time synchronization of competitive neural networks.

Chuan Chen1, Ling Mi2, Zhongqiang Liu3

  • 1School of Cyber Security, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China; State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications), Beijing 100876, China; Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center(National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 19, 2021
PubMed
Summary

This study achieves predefined-time synchronization for competitive neural networks (CNNs) using novel bilayer controllers. The controllers ensure synchronization within a user-defined time, independent of initial conditions.

Keywords:
Competitive neural networksPredefined-time synchronizationThe predefined time

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Area of Science:

  • Control Theory
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Competitive Neural Networks (CNNs) exhibit complex dynamics.
  • Achieving synchronization in CNNs within a fixed, predetermined time is a significant challenge.
  • Existing methods often lack precise temporal control over synchronization.

Purpose of the Study:

  • To investigate predefined-time synchronization for competitive neural networks (CNNs).
  • To design and validate novel bilayer controllers for achieving rapid and predictable synchronization.
  • To establish theoretical guarantees for synchronization within an arbitrary positive constant time.

Main Methods:

  • Development of two distinct bilayer controllers for CNNs.
  • Controller 1: Utilizes a sign function.
  • Controller 2: Employs an exponential function and Lyapunov stability analysis.
  • Theoretical analysis based on two predefined-time stability theorems.

Main Results:

  • Both designed controllers successfully achieve predefined-time synchronization for CNNs.
  • Synchronization is achieved regardless of the initial states of the networks.
  • The predefined synchronization time is a tunable parameter within the controllers.
  • A simulation example validates the effectiveness of the proposed controllers.

Conclusions:

  • The proposed bilayer controllers offer effective solutions for predefined-time synchronization of CNNs.
  • The controllers provide precise temporal control over network synchronization.
  • This research contributes to the stability and control of complex neural network systems.