Optimization control for mean square synchronization of stochastic semi-Markov jump neural networks with non-fragile hidden information and actuator saturation
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View abstract on PubMed
Summary
This summary is machine-generated.This study addresses control and synchronization for stochastic hidden semi-Markov jump neural networks with actuator saturation. A novel non-fragile controller ensures reliable synchronization despite uncertainties.
Area Of Science
- Control Theory
- Artificial Intelligence
- Stochastic Systems
Background
- Stochastic hidden semi-Markov jump neural networks (SMJNNs) present complex dynamics.
- Actuator saturation and system uncertainties challenge reliable control.
- Achieving robust synchronization in these networks is crucial.
Purpose Of The Study
- To investigate asynchronous output feedback control and H∞ synchronization for SMJNNs.
- To develop a non-fragile controller robust to uncertainties and mode mismatches.
- To establish sufficient conditions for stochastic mean square synchronization (MSS).
Main Methods
- A novel NN model integrating semi-Markov process (SMP), hidden information, and Brownian motion.
- Design of a non-fragile controller leveraging hidden information for anti-interference.
- Construction of a Lyapunov function based on SMP to ensure MSS within the domain of attraction.
Main Results
- Sufficient conditions for achieving stochastic mean square synchronization (MSS) are derived.
- The proposed non-fragile controller effectively mitigates uncertainties and enhances system reliability.
- Demonstrated feasibility of the control strategy through numerical examples.
Conclusions
- The developed control method ensures robust and reliable H∞ synchronization for complex SMJNNs.
- The integration of SMP and hidden information provides a powerful framework for modeling and control.
- The findings contribute to advancing control theory for uncertain stochastic systems.
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