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Finite-time stabilization and energy consumption estimation for delayed neural networks with bounded activation
Chongyang Chen1, Song Zhu1, Min Wang1
1School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China.
Summary
This study introduces a novel switch controller for finite-time stabilization of delayed neural networks (DNNs), also estimating energy consumption. Results generalize prior work on DNN stability and energy efficiency.
Area of Science:
- Control Theory
- Computational Neuroscience
- Applied Mathematics
Background:
- Delayed neural networks (DNNs) are crucial in modeling complex systems but present challenges in stability analysis.
- Finite-time stability is essential for practical applications requiring rapid system response.
- Energy consumption is a critical factor in the efficiency of control systems.
Purpose of the Study:
- To develop a finite-time stabilization strategy for DNNs with bounded activation functions.
- To estimate the energy consumption associated with the proposed control method.
- To extend existing theoretical results in neural network control and energy analysis.
Main Methods:
- A novel switch controller is designed based on the comparison theorem.
- Finite-time stability is proven under bounded activation function conditions.
- Inequality techniques are employed for energy consumption estimation.
Main Results:
- The proposed controller guarantees finite-time stability for the considered DNNs.
- A method for estimating the energy consumption of the control system is established.
- The findings generalize and improve upon previous research in the field.
Conclusions:
- The developed approach effectively achieves finite-time stabilization for DNNs.
- The energy consumption estimation provides valuable insights for system design.
- Numerical simulations validate the theoretical results and the controller's performance.
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