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Exponential H∞ Filtering for Continuous-Time Switched Neural Networks Under Persistent Dwell-Time Switching
IEEE Transactions on Cybernetics
|March 21, 2019
Summary
This study designs a mode-dependent H∞ filter for continuous-time switched neural networks (NNs) using a persistent dwell-time (PDT) strategy. The filter ensures global uniform exponential stability of the filtering error system.
Area of Science:
- Control Systems Engineering
- Computational Neuroscience
- Applied Mathematics
Background:
- Switched neural networks (NNs) present complex dynamics due to mode transitions.
- Exponential stability is crucial for reliable system performance in NNs.
- H∞ filtering is essential for attenuating noise and uncertainties in dynamic systems.
Purpose of the Study:
- To design a mode-dependent exponential H∞ filter for continuous-time switched NNs.
- To ensure global uniform exponential stability for the filtering error system.
- To develop criteria for filter design considering switching frequency.
Main Methods:
- Utilizing a persistent dwell-time (PDT) switching strategy for mode transitions.
- Employing Lyapunov functions and switched system theory for stability analysis.
- Applying a decoupling method for filter gain derivation.
Main Results:
- Criteria for the solvability of the H∞ filtering problem are established.
- The proposed filter guarantees global uniform exponential stability.
- Filter gains are computed using a straightforward decoupling approach.
Conclusions:
- The designed mode-dependent filter is effective for continuous-time switched NNs.
- The persistent dwell-time (PDT) strategy facilitates robust filter design.
- Numerical examples validate the filter's availability and performance.
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