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Event-Based Dissipative Filtering of Markovian Jump Neural Networks Subject to Incomplete Measurements and Stochastic
IEEE Transactions on Cybernetics
|November 6, 2019
Summary
This study develops an event-triggered filter for Markovian jump neural networks facing deception attacks and incomplete data. The method ensures system stability and dissipativity despite communication vulnerabilities and random attacks.
Area of Science:
- Control Systems Engineering
- Networked Systems
- Artificial Intelligence
Background:
- Markovian jump neural networks are susceptible to deception attacks and incomplete measurements.
- Vulnerabilities in communication networks can compromise system performance by injecting malicious data.
- Event-triggered communication is crucial for efficient data transmission in resource-limited networks.
Purpose of the Study:
- To investigate dissipativity-based filtering for Markovian jump neural networks under deception attacks and incomplete measurements.
- To design an event-triggered communication strategy to reduce communication load while maintaining system integrity.
- To ensure the stochastic stability and dissipativity of the filtering system despite adversarial conditions.
Main Methods:
- Adoption of an event-triggered communication strategy with mode-dependent triggering conditions.
- Development of a filtering approach for systems with random, Bernoulli-distributed deception attacks.
- Derivation of sufficient conditions for stochastic stability and dissipativity of the augmented system.
Main Results:
- The proposed event-triggered filter effectively handles incomplete measurements and deception attacks.
- Sufficient conditions guaranteeing stochastic stability and dissipativity were successfully derived.
- Numerical simulations validated the theoretical findings and the effectiveness of the proposed method.
Conclusions:
- The developed dissipativity-based filtering approach with event-triggered communication is robust against deception attacks and data incompleteness in Markovian jump neural networks.
- The strategy enhances system performance and reliability in vulnerable communication environments.
- The findings provide a theoretical foundation and practical solution for secure and efficient filtering in networked neural systems.
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