Adaptive synchronization for fuzzy inertial complex-valued neural networks with state-dependent coefficients and mixed delays

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.

Automated summary

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.