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Aperiodically intermittent quantized control-based exponential synchronization of quaternion-valued inertial neural networks

  • 0College of Mathematics and System Sciences, Xinjiang University, Urumqi 830017, China.
Neural Networks : the Official Journal of the International Neural Network Society +

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September 3, 2024

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September 3, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study explores exponential synchronization in quaternion-valued inertial neural networks using aperiodically intermittent quantized control. The findings offer insights into complex network dynamics and efficient control strategies.

Area Of Science

  • Complex Systems
  • Control Theory
  • Artificial Neural Networks

Background

  • Inertial neural networks (INNs) exhibit complex dynamics due to added inertia terms, differing from traditional models.
  • Aperiodically intermittent quantized control offers advantages in reducing communication load and control costs compared to continuous control.

Purpose Of The Study

  • To investigate the exponential synchronization of quaternion-valued inertial neural networks (QV-INNs).
  • To apply a novel aperiodically intermittent quantized control strategy to these complex networks.

Main Methods

  • Development of a compact quaternion-valued aperiodically intermittent quantized control protocol.
  • Formulation of concise criteria using matrix inequalities.
  • Construction of a Lyapunov functional and application of a direct analysis approach.

Main Results

  • The proposed control protocol simplifies theoretical derivations for QV-INNs.
  • Concise criteria for achieving exponential synchronization were successfully derived.
  • The effectiveness of the method was validated through a numerical example.

Conclusions

  • The study successfully demonstrates exponential synchronization for QV-INNs under the specified control.
  • The developed criteria and control protocol are effective and simplify analysis.
  • This work contributes to the understanding and control of complex neural network systems.

Keywords:

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