@article{Xu2024, 
author = {Tian Xu and Ailong Wu},
title = {Stabilization of nonlinear hybrid stochastic time-delay neural networks with Lévy noise using discrete-time feedback control},
year = {2024},
journal = {AIMS Mathematics},
volume = {9},
number = {10},
pages = {27080-27101},
keywords = {stochastic time-delay neural networks, highly nonlinear, Lévy noise, discrete-time state and mode, stabilization},
url = {https://www.sciopen.com/article/10.3934/math.20241317},
doi = {10.3934/math.20241317},
abstract = {This paper aims to formulate a class of nonlinear hybrid stochastic time-delay neural networks (STDNNs) with Lévy noise. Specifically, the coefficients of networks grow polynomially instead of linearly, and the time delay of given neural networks is non-differentiable. In many practical situations, nonlinear hybrid STDNNs with Lévy noise are unstable. Hence, this paper uses feedback control based on discrete-time state and mode observations to stabilize the considered nonlinear hybrid STDNNs with Lévy noise. Then, we establish stabilization criteria of  H∞ stability, asymptotic stability, and exponential stability for the controlled nonlinear hybrid STDNNs with Lévy noise. Finally, a numerical example illustrating the usefulness of theoretical results is provided.}
}