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Mean-Square Synchronization of Additive Time-Varying Delayed Markovian Jumping Neural Networks Under Multiple
IEEE Transactions on Neural Networks and Learning Systems
|March 18, 2025
Summary
This study solves the mean-square synchronization problem for additive time-varying delayed Markovian jumping neural networks using multiple stochastic samplings. The proposed method enhances secure image encryption performance.
Area of Science:
- Control Theory
- Artificial Intelligence
- Network Security
Background:
- Additive time-varying delayed Markovian jumping neural networks (ATVMJNNs) present complex synchronization challenges.
- Existing methods often suffer from model conservatism and limited applicability.
Purpose of the Study:
- To address the mean-square asymptotic synchronization of ATVMJNNs.
- To apply these advancements to secure image encryption (SIE).
- To reduce model conservatism in synchronization analysis.
Main Methods:
- Development of a mode-dependent discontinuous Lyapunov-Krasovskii functional (DLKF).
- Introduction of an auxiliary slack-matrix-based integral inequality (ASMBII) for integral term approximation.
- Establishment of a multiple stochastic sampling framework.
- Derivation of stability criteria using linear matrix inequalities (LMIs).
Main Results:
- Achieved less conservative criteria for mean-square asymptotic stability.
- Demonstrated superior performance in numerical validations.
- Validated effectiveness through a practical secure image encryption application.
Conclusions:
- The proposed methods effectively solve the synchronization problem for ATVMJNNs.
- The techniques offer superior performance compared to existing approaches.
- The study provides a robust framework for both theoretical analysis and practical applications in secure image encryption.
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