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Aperiodically intermittent quantized control-based exponential synchronization of quaternion-valued inertial neural
Jingnan Fei1, Sijie Ren1, Caicai Zheng1
1College of Mathematics and System Sciences, Xinjiang University, Urumqi 830017, China.
Summary
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.
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