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H∞ State Estimation for Neural Networks With General Activation Function and Mixed Time-Varying Delays
IEEE Transactions on Neural Networks and Learning Systems
|August 22, 2020
Summary
This study presents a new method for H∞ state estimation in neural networks with mixed delays. The approach utilizes a novel Lyapunov-Krasovskii functional and generalized integral inequalities for improved accuracy and stability.
Area of Science:
- Control Systems Engineering
- Artificial Neural Networks
- Nonlinear Systems Analysis
Background:
- Neural networks with mixed delays present challenges in state estimation.
- Existing methods may be conservative or lack accuracy.
Purpose of the Study:
- To develop an H∞ state estimator for neural networks with mixed delays.
- To reduce conservatism and enhance the accuracy of the estimation model.
Main Methods:
- Construction of a novel parameterized delay-product Lyapunov-Krasovskii functional (LKF).
- Application of generalized free-weighting-matrix integral inequalities.
- Incorporation of a more general activation function with parameterized delay intervals.
Main Results:
- Sufficient conditions derived for asymptotic stability of the estimation error system.
- Prescribed H∞ performance achieved for the state estimator.
- Demonstrated benefits through numerical simulations.
Conclusions:
- The proposed method effectively addresses H∞ state estimation for neural networks with mixed delays.
- The novel LKF and integral inequality approach reduces estimation conservatism.
- The enhanced estimator model provides improved accuracy and guaranteed performance.
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