Journal
FUZZY SETS AND SYSTEMS
Volume 411, Issue -, Pages 174-189Publisher
ELSEVIER
DOI: 10.1016/j.fss.2020.05.013
Keywords
Fuzzy neural networks; Synchronization; Complex-valued neural networks; Adaptive control; Mixed delays
Funding
- Open Research Fund of the Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of Education (Anhui Polytechnic University) [GDSC202012]
- National Priority Research Project - Qatar National Research Fund [NPRP 9-166-1031]
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This paper considers a new class of fuzzy inertial complex-valued neural networks with state-dependent coefficients and mixed delays. The model is constructed and converted into two real-valued neural networks. Novel adaptive controller and synchronization criteria are designed and verified through theoretical analysis and numerical example.
In this paper, a class of fuzzy inertial complex-valued neural networks with state-dependent coefficients and mixed delays is considered. We construct the model of fuzzy inertial complex-valued neural networks with state-dependent coefficients and mixed delays for the first time. By dividing the fuzzy inertial complex-valued neural networks into real and imaginary parts, the model is converted into two fuzzy inertial real-valued neural networks. Then, we design a novel adaptive controller, and under the action of this controller, synchronization criteria for the fuzzy inertial complex-valued neural networks with state-dependent coefficients and mixed delays are obtained by Lyapunov's stability theory and the differential equation theory with discontinuous right side. Finally, a numerical example is given to show the correctness of results. (c) 2020 Elsevier B.V. All rights reserved. In this paper, a class of fuzzy inertial complex-valued neural networks with state-dependent coefficients and mixed delays is considered. We construct the model of fuzzy inertial complex-valued neural networks with state-dependent coefficients and mixed delays for the first time. By dividing the fuzzy inertial complex-valued neural networks into real and imaginary parts, the model is converted into two fuzzy inertial real-valued neural networks. Then, we design a novel adaptive controller, and under the action of this controller, synchronization criteria for the fuzzy inertial complex-valued neural networks with state-dependent coefficients and mixed delays are obtained by Lyapunov?s stability theory and the differential equation theory with discontinuous right side. Finally, a numerical example is given to show the correctness of results.
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