Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 52, Issue 6, Pages 4585-4595Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.3031379
Keywords
Neural networks; Switches; Delays; Switched systems; Stability criteria; Control systems; Neurodynamics; Complex-valued neural networks; event-triggered control; exponential stabilization; inertial neural networks; state-based switched
Categories
Funding
- Open Research Fund of the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Ministry of Education, Anhui Polytechnic University [GDSC202012]
- National Priority Research Project - Qatar National Research Fund [NPRP 9-166-1031]
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This article investigates the exponential stabilization issue of a class of state-based switched inertial complex-valued neural networks with multiple delays through event-triggered control. The networks are transformed into real-valued neural networks and a controller with transmission sequence is designed to achieve exponential stabilization. Sufficient conditions for exponential stabilization are obtained using Lyapunov functions and inequalities, and it is proven that the Zeno phenomenon will not occur.
This article explores the exponential stabilization issue of a class of state-based switched inertial complex-valued neural networks with multiple delays via event-triggered control. First, the state-based switched inertial complex-valued neural networks with multiple delays are modeled. Second, by separating the real and imaginary parts of complex values, the state-based switched inertial complex-valued neural networks are transformed into two state-based switched inertial real-valued neural networks. Through the variable substitution method, the model of the second-order inertial neural networks is transformed into a model of the first-order neural networks. Third, an event-triggered controller with the transmission sequence is designed to study the exponential stabilization issue of neural networks constructed above. Then, by constructing the Lyapunov functions and based on some inequalities, we obtain sufficient conditions for exponential stabilization of the proposed neural networks. Furthermore, it is proved that the Zeno phenomenon cannot happen under the designed event-triggered controller. Finally, a simulation example is given to illustrate the correctness of the results.
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