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New delay-dependent exponential H(infinity) synchronization for uncertain neural networks with mixed time delays
Hamid Reza Karimi1, Huijun Gao
1Faculty of Technology and Science, University of Agder, 4898 Grimstad, Norway. hamid.r.karimi@uia.no
Summary
This study introduces a novel exponential H(infinity) synchronization method for uncertain master-slave neural networks with mixed time delays. The proposed control law ensures synchronization despite uncertainties and various time delays.
Area of Science:
- Control Systems Engineering
- Computational Neuroscience
- Nonlinear Dynamics
Background:
- Master-slave neural networks (MSNNs) are crucial for complex system modeling.
- Synchronization in MSNNs is challenging due to mixed time delays and uncertainties.
- Existing methods often require restrictive conditions.
Purpose of the Study:
- To develop an exponential H(infinity) synchronization method for uncertain MSNNs.
- To address mixed time delays including neutral, discrete, and distributed types.
- To design a robust synchronization law under less restrictive conditions.
Main Methods:
- Utilized a discretized Lyapunov-Krasovskii functional.
- Employed free-weighting matrices for delay-dependent conditions.
- Designed a delayed state-feedback control law formulated using linear matrix inequalities.
Main Results:
- Established sufficient conditions for exponential H(infinity) synchronization.
- The proposed controller guarantees synchronization regardless of initial states.
- Demonstrated effectiveness through comparisons and numerical simulations.
Conclusions:
- The developed method provides an effective approach for synchronizing uncertain MSNNs.
- The approach is robust to polytopic and norm-bounded uncertainties.
- Numerical simulations validate the superiority and applicability of the synchronization laws.
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