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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
Published on: November 12, 2019
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A waiting-time-based event-triggered scheme for stabilization of complex-valued neural networks
Xiaohong Wang1, Zhen Wang2, Qiankun Song3
1College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China.
Summary
This study introduces an event-triggered control for complex-valued neural networks (CVNNs), reducing data transmission rates. The novel scheme enhances stability analysis and efficiently designs control and trigger parameters.
Area of Science:
- Control Theory
- Artificial Intelligence
- Complex Systems
Background:
- Complex-valued neural networks (CVNNs) present unique challenges in control system design.
- Event-triggered control strategies aim to optimize resource usage by reducing data transmission frequency.
Purpose of the Study:
- To develop an event-triggered control scheme for global stabilization of CVNNs.
- To reduce the data transmission rate in CVNN control systems.
- To derive less conservative stability criteria for the closed-loop system.
Main Methods:
- A waiting-time-based event-triggered scheme with an exponential decay term is proposed.
- An input delay approach is utilized to construct a time-dependent Lyapunov-Krasovskii functional.
- Matrix transformation is employed for the co-design of feedback gains and trigger parameters.
Main Results:
- The proposed scheme effectively reduces the data transmission rate.
- A less conservative stability criterion is formulated for the controlled CVNNs.
- The co-design method provides an efficient way to determine control and trigger parameters.
Conclusions:
- The developed event-triggered control scheme is feasible and superior for stabilizing CVNNs.
- The findings contribute to efficient and robust control of complex-valued neural networks.
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