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Reduced-order state estimation of delayed recurrent neural networks
He Huang1, Tingwen Huang2, Xiaoping Chen1
1School of Electronics and Information Engineering, Soochow University, Suzhou 215006, PR China.
Summary
This study introduces a novel reduced-order state estimator for delayed recurrent neural networks, ensuring system stability. The design relies on integral inequalities and linear matrix inequalities for effective state estimation.
Area of Science:
- Control Systems Engineering
- Artificial Intelligence
- Network Science
Background:
- Full-order state estimation is well-researched, but reduced-order estimation for complex systems remains challenging.
- Delayed recurrent neural networks (RNNs) present unique difficulties in state estimation due to time delays.
- Existing methods may not adequately address the stability and performance of reduced-order estimators in these networks.
Purpose of the Study:
- To develop a novel reduced-order state estimation method for delayed recurrent neural networks.
- To guarantee the global asymptotical stability of the error system for the proposed estimator.
- To provide a systematic design approach for the reduced-order state estimator's gain matrix.
Main Methods:
- Utilized integral inequalities to establish delay-dependent stability conditions.
- Developed a design methodology for reduced-order state estimators.
- Formulated the gain matrix determination as a solution to a linear matrix inequality (LMI).
Main Results:
- A delay-dependent approach for reduced-order state estimation in delayed RNNs was successfully proposed.
- Global asymptotical stability of the error system was rigorously guaranteed.
- The gain matrix of the estimator was shown to be derivable from LMI solutions.
Conclusions:
- The proposed method effectively addresses the reduced-order state estimation problem for delayed RNNs.
- The integral inequality and LMI-based approach ensures robust stability.
- Numerical simulations validated the practical effectiveness of the developed state estimation technique.
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