Neural-network-based asynchronous security control for delayed Markov jump systems with imperfect transition probabilities via dynamic-memory event-triggered mechanism

Key Points

• Effective neural network models enhance asynchronous control mechanisms, improving response times.
• The implementation of a dynamic-memory event-triggered mechanism demonstrates significant efficiency gains.
• Analysis focuses on systems with imperfect transition probabilities, emphasizing real-world applicability.
• The findings support the need for advanced control strategies in dynamic environments with delay.